LDI

Is the US Dollar still a cheap tail risk hedge?

2025-01-19T09:13:00.000-08:00

Is the US Dollar still a cheap tail risk hedge?

1. What do we mean by tail risk?

"Tail risk" is an amorphous term and can mean different things to different investors. When thinking about tail risk hedging it is therefore important to define what tail risk protection is being sought against and so understanding the specific nature of the tail risk should be preface any further discussion on hedging tail risk. Tail risk in this context refers to adverse changes in the current market value of a global portfolio financial assets which causes their values to become and potentially remain depressed for a period of time. The tail risk typically manifests through a fall in the value of public market assets with a muted flow through to private market asset due to valuation lags

2. What do we mean by hedging tail risk?

A precise or accurate tail risk hedge is generally known to be expensive. For example, buying a put option to protect against a (say) 10% fall in the value of the FTSE100 is often sufficiently costly that buying and holding (rolling) the hedge for a long period of time can erode most of the rewards from being invested in the FTSE100.

Proxy hedges are considered cheaper than precise hedges. Proxy hedges are assets whose values are expected in most circumstances to be inversely related to the asset for which protection is sought.

For example, assume a UK investor investing £100 equally split between the FTSE100 and S&P500. The investor is most concerned with protecting their investment in Pound terms. Despite this, our investor chooses not to hedge the currency risk from buying the S&P500 but instead accepts that if the US Dollar (USD) falls in value against the British Pound (GBP) then the investor will make a loss in GBP terms, all else being equal. One possible reason for taking this risk of Cable depreciation ("Cable" is a commonly used acronym for the Pound/Dollar exchange rate) depreciation is that the investment portfolio retains exposure to the US Dollar as a proxy tail risk hedge against adverse outcomes.

3. Why is the USD considered to be a proxy tail risk hedge?

Historically, when the outlook for the global economy is either very good or very bad then the US Dollar has tended to appreciate against other currencies but has a tendency to tread water when the global outlook is meh. This is often referred to as the "Dollar Smile"; a term apparently coined years ago by Morgan Stanley foreign exchange (FX) strategists, the 'dollar smile' theory argues that USD does best when things are either very good (arguably driven by US growth) or very bad (safe-haven demand).

The chart below shows this:

4. Will the Dollar still smile?

The USD has shown significant strength against most G10 crosses due to 1) relatively high US rates linked to 2) the strong outlook for US growth and potential inflationary consequences driven partly by so-called US exceptionalism.

In the event of a global recession, will the Dollar (at least) initially outperform other currencies and so provide the tail risk hedge shown in the chart above?

There are a few reasons to think that this time could be different

a) At the present time interest rate differentials between the US and other countries is relatively high. This means that a US investor investing in global equities (ex US) probably hedges any FX exposure back into USD to avoid paying away the high yield on his or her US dollars or because they don’t have a view on the offshore currency vs the USD. In the event of a global recession, if these investors view the "risk aversion" trade as unwinding offshore equity holdings and repatriating this to the US, they would also be unwinding the currency hedge and so there is no net positive impact on the value of the USD from this repatriation trade.

=> Net impact = neutral for USD

b) That said, US investor positioning is currently skewed to US equities (including the Mag 7) at the expense of global equities (ex US).

A global recession and US equity sell-off may, if coupled with the Fed cuts lowering yields on US bonds, prompt some of these US investors to consider that rebalancing out of US equities into global equities on an unhedged basis is a risk-reducing trade. [Falling US rates reduces the negative carry from being invested in non-USD denominated equities and so reduces the potential opportunity cost of not hedging the currency risk]

=> Net impact = negative for USD

c) If the US is cutting rates in response to recession fears then this reduces the interest rate differential to other currencies making it more attractive for foreign investors in US equities to hedge their USD risk back to their home currency.

=> Net impact = negative for the USD.

d) Unlike in previous times of market stress when foreign central banks experienced a shortage of US Dollars, swap lines agreed between these central banks and the Fed means that the USD supply/demand imbalance which held up the Dollar will not have the same impact in the future.

=> Net impact = neutral for the USD

Taking it all together there are good reasons to believe that in a global recession scenario the USD may not provide the tail risk hedge it once did.

2023-04-30T07:07:00.000-07:00

Links to

Dalriada Trustees Presentation

Speech by Charlotte Gerken

PPF Strategic Plan: an observation for private market investment managers

2019-04-10T07:31:00.002-07:00

The PPF released it's 3-year strategic plan today.

The statement that caught my eye was

"In the future we expect there to be fewer claims from schemes on the PPF than today, and therefore the levy we need to collect will be small in comparison to our own assets and liabilities. Our current projection is that this point will be be reached in 2030."

Might the timeframe for a decline in total risk-based levies paid to the PPF tell us something about the pace at which the c. £1.5trn corporate DB pension fund industry will pivot away from equity and towards mezzanine and senior debt?

1. The PPF think that DB pension funds reach a tipping point in 2030 where the PPF will not be collecting nearly the same amount of levies as it does today.

2. The majority of levies collected is from schemes with a buy-out deficit. Fully-funded schemes pay a trivial amount in levies.

Schemes in deficit on a buy-out basis pay a levy of approximately 10 basis points of that buy-out deficit.
Schemes that are fully funded on a buy-out basis pay a levy of approximately 1/10 of one basis point of their total buy-out liabilities.

3. The PPF's suggestion of a tipping point in 2030 therefore seems to suggest that they are planning for a world where a significant number of schemes are fully funded on a buy-out basis by 2030.

Of course a best-case scenario for financial soundness of DB pension funds corresponds to a worst-case scenario for future levy income for the PPF and so perhaps the PPF is being overly cautious in its planning.

4. If, however, this scenario were to come to pass, it may have implications for asset managers and the types of products that they will need to develop, as well as for the sources of capital available to fund unlisted investments such as infrastructure.

For example, much is made of the institutional investor trend toward private market assets. Under the sort of scenario outlined by the PPF, DB pension funds invested in private markets are increasingly likely, over time, to switch their private market exposure away from equity and towards mezzanine and senior debt.

In the run-up to achieving fully-funded status, equity and mezzanine debt (with its higher returns) could feature more heavily as funds seek to close funding gaps. As fully funded status is reached, senior, secured debt with lower, but more stable returns, may well dominate.

Now you could say we have always know this but the scenario outlined by the PPF may allow us to place a timeline on this.

Private market equity (infra, real estate and corporate) will continue to be gradually squeezed out of DB fund allocations over the next 11 years (2019-2030) as funding levels improve and reduce the need for growth assets.

In the 1st half of the next decade (2020-205), pension funds may close their funding deficits by continuing to allocate to private market equity.

Gradually, as funding levels improve, mezzanine and junior tranches may see higher demand and benefit from early flows away from equity. This may happen if mezz debt is perceived to offer favourable returns to allow (smaller) funding gaps to be closed but at lower volatility compared to investments in equity.

As funding levels improve further still, senior debt may gain favour

The pace at which this all happens will clearly depend on how well funded pension funds actually become over the next decade.

One could say that private market equity may well become a a victim of its own success by allowing better funded pension plans to more quickly reverse out of equity and into mezzanine and senior debt.

5. For asset managers raising private market funds in the next few years, equity funds will remain attractive but increasingly these managers may wish to consider diversifying their product mix to offer capabilities and funds that invest in other parts of the capital structure, including mezzanine and senior debt.

6. Private market investment managers with strong credentials in equity investment should be ideally placed to understand the risks of investing in mezzanine debt and hold a competitive edge over those asset managers with more experience in managing debt. That competitive advantage may erode with time.

The Impact of Physical Climate Risk on CDI and Secured Income strategies

2019-04-06T04:56:00.001-07:00

So called Secured Income Funds may invest in Commercial Mortgage Backed Securities. Whilst not a significant holding, it is nevertheless an asset class used by many managers of Secured Income strategies and one worth understanding a bit better. Holdings in CMBS by Secured Income Funds vary but may be between 5-15%.

So, what are the implications for risk-adjusted returns on CMBS that arise from the recent study by BlackRock and Rhodium, into climate-related risks?

The BlackRock study focuses on the US CMBS market but the key observation may well translate into other markets and so investors would do well to engage their asset managers on the issues raised.

Turning to the key points from this latest research:

1. Hurricanes give rise to flood and wind risk.

2. Commercial mortgages within CMBS pools are required to have insurance against wind risk.

3. However, flood risk for CMBS mortgages is, typically, only insured in FEMA-designated areas (again a specific feature of the US CMBS market and not applicable to European or UK CMBS).

4. Hurricane risks are increasing and expected to increase further

BlackRock highlight past and expected future changes in hurricane risk for US properties underpinning commercial mortgages in the CMBS market as follows:

- a 137% increase since 1980 and
- a 275% expected increase by 2050 assuming "no climate action" is taken.

5. If the losses arising from flood risk (and other climate-related changes) are not properly allowed for then valuations in the CMBS market may be underestimating the true physical risk to the underlying collateral pool from changes to our climate. This may mean that risk-adjusted returns are overstated.

[SB observation: this risk also applies to insured flood risks. For insured risks, risk-adjusted returns should allow for higher premiums in the event that insurance rates tighten. This may be especially true if insurers are underpricing the true risk from climate change.]

So what could UK pension funds who implement CDI, through Secured Income strategies, do?

The key point is that when allowing for expected returns (net of expected losses) from a Secured Income strategy, it will become increasingly important to build in some prudence for higher expected losses to the underlying collateral pool that may arise from climate-related risks.

Taken on its own, a CDI strategy that has less than 5% of total fund assets invested in CMBS may not be sufficiently adversely impacted by a small deterioration in expected losses. [E.g. in one modelled scenario, BlackRock estimate a 60bps increase in expected losses for US CMBS.]

However, adding in Residential MBS and other loans (private infrastructure debt and private real estate debt) that underpin Secured Income strategies, may well increase the overall impact on expected losses due to climate risk.

UK pension funds appear to be paying more attention to potential investments in junior infrastructure debt and mezzanine real estate debt at the expense of senior debt. This appears to be linked to a "crowding-out effect" -senior, investment-grade debt is being bid-up by UK annuity books who view this part of the debt market as offering attractive returns after allowing for the favourable capital treatment that Solvency 2 regulations afford to senior, secured, investment-grade debt. Unexpected losses, from unforeseen climate risks, are more likely to be absorbed by junior and mezzanine tranches, which rank lower in the capital structure than senior debt holders. So, to the extent that UK pension funds are considering debt investments lower down in the capital structure, then climate-related risks may be more likely to "bite" and it becomes more pressing to allow for this risk.

Given the recent focus by government on the action being taken by the UK's largest pension funds in addressing the impact of climate-related risks, perhaps this study from BlackRock, together with the observation of how Secured Income strategies are impacted, may provide a catalyst to revisit expected loss assumptions and ensure that self-sufficiency discount rates are set having considered, in more detail, the climate-related risks in the underlying loan collateral pool?

A word (or three) of caution. Clearly when the largest asset manager in the world raises an issue, it will garner attention but it would be incorrect to assume that other key participants in this market have been oblivious to the risk being highlighted.

In fact, a recent article from Bloomberg highlights that these risks are, not only, well-known by market participants but many market participants including banks, rating agencies and asset managers have applied themselves to quantifying these risks.

A gratuitous plug for my own employer, DWS and their recent report on climate risk which was equally comprehensive and insightful.

Four Twenty Seven is another organisation at the forefront of quantifying climate-related risks in the Real Estate world as evidenced by this recent report into the impact on the REITs market.

2017-12-21T23:08:00.003-08:00

Jeff Bezos has plenty of reasons to smile. Amazon, the company he started in 1994, today, has a market capitalisation of $559bn. If Amazon was a country it would be 23rd largest country in the world ranking just below Switzerland.

Not that it is even a glint in the corner of his eye but Jeff's retirement plan couldn't be in better shape; Jeff's 16% shareholding in Amazon Inc is valued at $91bn. And if Mr Bezos was his own sovereign nation, he would rank as the 66th largest country - somewhere between the Ukraine and the Slovak Republic and larger than Sri Lanka.

But could Mr Bezos, having secured his own retirement, help secure the retirement of millions of his users. And even if his cause was less noble, could Amazon catapult its market capitalisation to the next level by bringing about an ‘Uber’ style revolution in the world of pensions.

How could this happen?

In today’s saving environment, retirees now find that their retirement income levels depend mostly on their own discipline to save during their working lives.

Millions of today's savers are face an uncertain financial future in retirement due to a once-in-a-generation shift in investment risk from companies to employees. The closure of defined benefit pension funds and the transition to defined contribution (DC) arrangements means that, in the future, retirees from a DC arrangement can no longer rely on access to a retirement income that bears a high degree of resemblance to their working income just prior to retiring or to an income that is inflation-protected. Rather, the shift to DC means that the adage ‘what you put in is what you get out’ has never been more apt when it comes to pensions.

But, as the pictures show, too many people are simply not saving enough.

We may choose to blame a change in the values of millennials versus baby-boomers or a cultural acceptance of instant gratification. Either way, too many people are simply not saving enough to ensure a comfortable retirement.

And, to compound matters, saving for retirement, when it does happen, tends to start later in life.

Most of us only sober up to the need for a retirement plan when we start a family or hit mid-life. The power of compound interest, whilst still effective at this later age, would have been even more effective if our saving habit kicked in a bit earlier. To make matters worse, marriages are occurring much later (if at all) and children, often a catalyst for future planning, are being had later in life.

In summary, how do we get the world saving more, and from an earlier age, to provide for their retirement?The answer may lie in a simple, yet powerful observation, that saving and spending are two sides of the same coin.

Human nature would appear to pre-dispose us to spending on items that meet our immediate needs, often at the expense of saving for a longer-term goal. What if we could tap into this natural propensity to spend and turn it into a savings plan; a savings plan on auto-pilot.

What if, for each pound spent, a little bit of that pound could be automatically squirreled away?

So how could one of the most recognisable sales platforms, Amazon, be turned into the world’s most powerful savings platform?

In two simple steps.

1. By allowing users to establish an Amazon profile that automatically, at checkout, added, say, 10% to cost of the basket.

2. By automatically directing that ‘10%’ into a retirement savings account, either a default account administered by Amazon or a plan chosen by the user, for example, an existing 401(k) plan in the US or self-invested personal pension in the UK.

Simple, yet effective.

Of course, we could continue to develop on this theme and rethink the savings ecosystem around this simple, yet basic concept.

Other possibilities include:

At the customer’s election, Amazon could link up with the local tax collecting authority (e.g. the IRS or HMRC) so that, at the time of tax filing, the tax credit emanating from this pension contribution was identified and paid directly back into the “Amazon” retirement savings account rather than being refunded into a bank account.
In the event that other retailers offered their customers the same option to set up their online buying profiles in a similar way then, Amazon, having built the plumbing to facilitate and track savings, could allow other online retailers access to the default Amazon savings platform.

Back to Amazon and Mr Bezos. Revenue at Amazon last year was approximately $135bn, of which $90bn was attributable to retail sales. With a 20% take-up rate of this ‘auto-pilot’ retirement savings option, and a 10% 'contribution rate', Amazon could be helping their customers squirrel away $1.8bn every year. This amount could be multiples higher than this, in the event that customers began to view Amazon as their primary retirement savings vehicle. It isn’t hard to imagine Amazon building a retirement savings business, that in a short span of time, accumulated assets under management well in excess of $100bn. It would also make Amazon a Top 200 asset management firm, ranked by assets under management. Something that Amazon’s shareholders may well come to appreciate.

For those of you thinking that this doesn't sound core to Amazon’s business of being an online retailer, it may be worthwhile pointing out that Amazon’s core profit making business is not its online retail business. Whilst the majority of Amazon’s revenuecomes from online sales, the online retail sales business is not the major driver of profits. Amazon’s profits come from a more obscure part of its business, its Amazon Web Services (AWS) – a part of the business that provides outsourced cloud computing services. For example, Netflix uses AWS for almost all its backend infrastructure, storing and streaming of its web series.

So, should Amazon wish to add a further revenue stream through providing financial services, and more importantly financial security, it could do worse than consider exploring the world of pension saving.

In summary, whilst this blog considers a specific example of how Amazon could enable their customers to save more for retirement, the more general point is to question our current paradigm of separating spending and saving ecosystems. The exception may be 'rewards' programmes (e.g. earning air miles for spending) but one could argue that even this does not go nearly far enough towards integrating the two ecosystems.

More specifically, by looking into innovative ways in which we can redesign how we link our savings ecosystem more directly with our spending ecosystem, we may stand a better of chance of introducing a disciplined and consistent approach to saving for retirement. One that begins with the very first pay cheque.

Assessing the liquidity risk premium in government-guaranteed bonds

2016-11-03T06:58:00.000-07:00

There are two main risks when investing in government-guaranteed, inflation-linked, Eskom bonds.

1. Credit risk

The government guarantee on these bonds means that these bonds are free from Eskom credit risk. In our view the guarantee wording is suitably robust. That said, we would observe that, in the event of a sovereign stress event that requires some form of waterfall or priority to be set for meeting sovereign obligations, then it would not be unreasonable for the sovereign to primarily focus on firstly meeting the sovereign obligations before turning to the government-guaranteed obligations.

This means that, in our view, there should be some compensation for this risk, even if it is small.

2. Liquidity risk

Eskom is the only State-Owned Enterprise (SOE) that makes a market in its own bonds. This, together with the fact that Eskom has the largest SOE bond issuance programme, makes Eskom bonds the most liquid of all the SOE-issued bonds available to a South African investor. That said, this liquidity is bifurcated with the nominal bonds generally being more liquid than inflation-linked bonds.

In our opinion, this lack of liquidity, relative to an equivalent sovereign bond, should also be

compensated for.

The purpose of this post is to consider the suitability of Eskom’s government-guaranteed, inflation-linked bonds for purposes of improving the yield on an LDI portfolio. In particular, which of the following two alternatives should be preferred:

1) Buying the physical Eskom bond and earning liquidity and credit risk premia

2) Buying an inflation-linked government bond and then repoing this bond out to raise cash to be invested in credit assets.

In order to answer this question we adopt the following approach. We acknowledge that this other approaches are possible:

We compare the yield on an Eskom government-guaranteed, inflation-linked bond to that available on a sovereign-issued bond with a similar modified duration* in order to calculate the additional yield earned over sovereign bonds. This provides us with an estimate of the liquidity risk premium that Eskom bonds offer, assuming of course that the guarantee is robust and that, for this reason, investors do not demand any additional premium for credit risk over the sovereign.
We can aim to quantify the credit risk premium in Eskom bonds by looking at the difference in yield between government-guaranteed Eskom bonds and the non-government guaranteed bonds issued by Eskom
We then aim to quantify the market’s implied pricing of the lower liquidity in Eskom government guarantee bonds using the repo market, which is only one measure of liquidity risk.
If we can demonstrate that the (repo?) market’s implied pricing is greater than the additional spread being earned on Eskom bonds over sovereign bonds then we may be able to conclude that perhaps the additional yield on Eskom bonds is not sufficiently compensated for the risk.

Step 1: Identify a market that can be used to estimate the liquidity risk premium on Eskom government-guaranteed bonds

To do this we want to consider market observable data in order to arrive at the market’s implied price for the lower liquidity of Eskom
government-guaranteed bonds.

One way of quantifying this is to look at the repo market. The repo market allows us to create liquidity on an Eskom bond by repoing out the bond in exchange for cash. The table below shows recent repo pricing received from one of the Big 4 SA banks.

Govt Guaranteed Bonds
HC @ 5%	Bid (Bps)	Offer (Bps)
1 month (1m JIBAR +)	85	10
3 month (3m JIBAR +)	90	15

This table (see ‘bid’ column) shows that the cost of a 3-month repo on Eskom government-guaranteed bonds is 3-month JIBAR plus 90bps.

- We note that this pricing applies to nominal, government-guaranteed bonds and not to inflation-linked bonds

- Pricing on inflation-linked bonds would be different – either the spread would be higher (ie higher than 90bps) or the haircut would be higher.

- For now, we ignore this complication which would only serve to further increase the market implied price for the liquidity premium that should
be earned on an Eskom bond.We note that this pricing applies to nominal, government-guaranteed bonds and not to inflation-linked bond

Based on our trading experience, the cost of a 3-month repo on inflation-linked government bonds has been approx. 3-month JIBAR plus 50bps.
3-month JIBAR is currently 7.36%.
This means a 3-month repo on

- an Eskom government guaranteed bond would cost 7.36%+0.9% = 8.26% to fund R95 of such
a bond.

- a government bond would cost 7.36% plus 0.5% = 7.86% to fund R100.

At first glance this may suggest a liquidity premium of 40bps. However, this estimate ignores the haircut on the Eskom bond.

Step 2: Adjust the ‘raw’ market price for the impact of the haircut

We now adjust the effective financing rate on the Eskom repo to allow for the fact that only R95 of the bond is funded compared to R100 of funding released from the repo of a government bond.

To release R100 of cash on the Eskom bond would require us to repo R100/0.95 = R105.26 of Eskom bonds
Financing this R105.26 of Eskom bonds would cost 8.26% of R105.26 = R8.69.
Therefore the equivalent cost of raising R100 cash from an Eskom bond is R8.69 vs R7.86 for a government bond.

The repo costs of each can now be compared, on a like-for-like basis, as follows

Government bond = 7.86% = 3-month JIBAR plus 50bps.
Eskom bond = 8.69% = 3-month JIBAR plus 133bps

When viewed in this way then the liquidity risk premium on Eskom bonds should be at least 83bps to compensate us for the higher cost of funding cash using an Eskom government-guaranteed bond vs a government bond. We note that this 83bps is likely to underestimate the market’s true cost because the repo market pricing used here applies to the more liquid, fixed-rate, government-guaranteed Eskom bonds. Pricing on the less liquid, inflation-linked equivalent is likely to be higher and this would then imply an even higher liquidity premium.

Step 3: Identify the additional yield that is available on Eskom government-guaranteed bonds over government bonds

Eskom-issued, government guaranteed, inflation-linked bonds have traded (over long periods of time) at a spread (above matched-maturity government bonds) that is not too far off 60bps.

We compare this 60bps to the 83bps implied by the repo market and conclude that based on this measure, Eskom bonds do not offer nearly enough compensation for the additional cost of using them to fund a cash investment in credit. Put differently, assuming that Eskom government-guaranteed bonds have no credit risk and only liquidity risk, then, based on pricing derived from the repo market, these bonds should offer us at least 83bps of liquidity premium.

For this reason we would generally have a stronger preference to buy government bonds and use them to fund a position in credit using the repo markets. That said, Eskom inflation-linked bonds offer a valuable source of inflation-linked duration in a market where this is in short supply. For those investors with large hedging programmes, it could be argued that investing in Eskom inflation linked bonds still makes sense because the tight spread to the sovereign is a direct reflection of the scarcity premium attached to quasi-government inflation-linked assets.

Monitoring credit risk in South African fixed income portfolios

2015-09-03T04:46:00.005-07:00

By Shalin Bhagwan and Jana Kershaw

Who should read this?

Investors in fixed income assets, including, but not limited to, those invested in:
Bond funds such as those benchmarked to the ALBI, CILI.
Cash funds such as those benchmarked to STeFI.
Balanced funds with allocations to bond and/or cash funds.
Credit funds aiming to outperform cash returns by investing in corporate bonds or similar instruments.

What is the key client takeaway?

· Fixed income fund managers typically target excess returns above their benchmarks and many will do so by taking credit risk i.e. lending investors’ money to borrowers who pay a premium to compensate the lender for the risk of non-payment.

· Measuring credit risk is a complex task and credit rating agencies are professional firms that measure credit risk associated with individual borrowers.

· A fourth credit rating firm, GCR, has now been recognised by the SARB. Over time we would expect GCR to increase their market share (more local debt issues rated by GCR) and this will place greater onus on fixed income managers to ensure they are consistently interpreting credit ratings from different providers.

· Investors should take care to ensure that reporting on credit rating exposures correctly classifies securities to avoid a portfolio being classified as being of a higher credit quality based on credit ratings alone without any adjustment for different rating methodologies used by different rating agencies.

· The benefit to investors would be to ensure that the amount of risk being taken in their fixed income portfolios is consistent with their risk appetite.

-----------------------------------------------------------------------------------------------------------------------

Credit ratings are an important tool used by fixed income managers and their investors to control overall risk at portfolio level. Whilst a credit rating cannot mitigate or improve the risk of any individual debt security, when these ratings are used to set upper limits on the amount of an investor’s portfolio that can be exposed to securities with a certain rating, then this can help set the expected maximum loss from taking credit risk. In addition, investors can also dictate the amount of risk they are willing to take by setting limits, for example, with regard to the minimum credit rating of companies whose bonds they invest in. For example, “investment grade” bonds are bonds that are issued by companies considered to be of the highest credit quality and will have credit ratings that are above a universally accepted threshold. By stating that they only wish to invest in investment grade bonds, investors and their fund managers place an upper bound on their credit risk tolerance.

The South African Reserve Bank’s (SARB) approval last week of Global Credit Ratings (Pty) Ltd (“GCR”) as an External Credit Assessment Institution is positive news. Previously only the large 3 global agencies (S&P, Moody’s and Fitch often collectively referred to as the “Big 3”) were approved by the SARB.

The endorsement by the SARB may well provide additional comfort to bond investors who may have previously been reluctant to rely exclusively on a GCR rating.

This development may therefore mean that fixed income portfolio managers could (if they have not already done so) consider widening the universe of companies they can invest in, if current internal and/or client-driven restrictions meant that only a credit rating from the Big 3 would have sufficed. The SARB approval may well provide the required comfort to lift any implicit restrictions on the use of GCR.

1. Credit ratings as a risk measure* remain valid but different vendor methodologies means that these differences have to be understood to ensure consistency…..

While most agencies would implement some form of ratings uplift for secured instruments, the differences in methodologies relating to upward notching of ratings to reflect security** may produce varying results. While the GCR secured-bond methodology allows for rating uplift based on the impact that the security has on overall recovery prospects, other agencies’ notching may be dependent on both the recovery prospects as well as the starting point of the rating (higher-rated entities get less benefit for security than lower-rated entities). Fixed income teams and credit analysts who have intimate knowledge of the differences in ratings agencies’ methodologies will be better placed to navigate these differences and, specifically in the case of split ratings (where the same entity is rated differently by multiple rating agencies), will be better placed to consolidate different ratings into a single comparable rating. [For those interested in reading more about the limitations of credit ratings we would refer to our previous article on this topic which can be found by clicking on this link: http://ldihedge.blogspot.com/2014/08/guns-dont-kill-people-people-kill.html

*Credit quality is assessed through two measures; a probability of default (“PD”) and loss given default (“LGD”). The product of these gives the “Expected Loss”. For example, a bond may have a PD of 1% and a LGD of 50%. This means that the expected loss assuming a R100 investment in that bond would be 50c (R100 x 0.01 x 0.5).

** Typically “security” arises when a borrower provides a lender with a priority claim on clearly earmarked assets (tangible and/or non-tangible) in the event that the borrower runs into difficulties in meeting its obligations to the lender. The provision of security by the borrower may or may not be a pre-requisite for the lender.

2. …and having recognised these differences, then reporting systems may need to be adapted, perhaps manually, to ensure that the correct reporting reaches clients.

This will not be new information for fixed income managers and it is likely that such adjustments will be made even though these adjustments will most likely have to be done manually. It is important for the asset manager to make sure that such adjustments are correctly captured by reporting systems that may well be designed to produce automated information based on unadjusted credit ratings.

[In this note we do not aim to cover, the potentially thorny question of, how these adjustments will be made and whether a subjective judgement may lead to inconsistencies across one or more asset managers].

Client and consultants should ensure that reporting is accurately captured for the purposes of monitoring their managers as well as controlling for credit risk at aggregate fund level.

3. In some cases, definitions in fixed income mandates may need to be updated / clarified to ensure that the portfolio manager has the scope to make use of a GCR rating

Some large pension fund investors have awarded stand-alone, segregated credit mandates and for these investors it may well be important to revisit the wording in their mandates around the use of credit rating agencies.

- Where the wording is broad enough to automatically allow the use of GCR as a rating agency (some clients have restricted the use of ratings to those provided by the “Big 3”) then investors will wish to ensure that any standardisation that may be necessary is carried out for reporting purposes.

- Where the wording is not sufficiently broad so as to allow the use of GCR then consideration should be given to broadening the wording of the mandate to allow the use of GCR. However, in this case, we would highlight one further challenge that arises where GCR is included in a manager’s universe of rating providers for the first time: if a mandate’s requirement does not prescribe the choice of credit rating (for example, by prescribing the use of the lowest rating across providers) then if GCR’s ratings are, on average, higher than those of the Big 3, then taking the highest rating across all four rating providers could optically improve the perceived credit worthiness of the portfolio.

4. GCR ratings may take on greater prominence in South Africa given that they tend to be cheaper to obtain

Finally, there are good reasons, mostly relating to the cost of obtaining a credit rating, as to why GCR ratings may be preferred, especially by debt issuers and fund managers seeking a rating on previously unrated debt. Given that GCR is locally based, their costs tend to be mainly Rand-denominated and so their pricing tariff for carrying out rating assessments reflects this. The Big 3 agencies tend to have (US) dollar-denominated costs and this drives up their cost for awarding a rating. In this case, it will become increasingly important to understand the subtle differences between each agencies ratings methodology and the impact on the credit rating that is awarded.

Other reasons why GCR may take on greater prominence include:

- The ease of access to a locally based credit rating team

- It may be argued that a locally based team may be better placed to understand the idiosyncrasies of the South African debt landscape (however we accept that there are equally valid counter arguments).

The bluffers guide to covered bonds

2015-06-06T11:44:00.001-07:00

Bluffers guide to Covered bonds

Next week the World Bank is in South Africa to discuss covered bonds with the local association of investment managers. For Uber taxi-drivers lucky enough to ferry our hard-currency, well-heeled visitors from abroad, here’s the bluffer’s guide to covered bonds. You may find it useful in striking up a conversation and earning that sought after five-star rating for your driving experience.

The South African banking regulator’s position on covered bonds	On May 23^rd 2011 the South African Reserve Bank’s (SARB) Office of the Registrar of Banks issued a guidance note banning South African banks and branches of foreign banks from issuing covered bonds or from engaging in any structured transaction that was the economic equivalent of a covered bond. As covered bond ranks senior to uninsured depositors as well as to senior, unsecured creditors, the Bank Supervision Department (BSD) of the SARB was concerned with protecting depositors. As South Africa does not have any scheme protecting depositors, the implications of covered bonds on the local depositor base would be different to that in, say, the UK where depositors are protected for the first £85,000 of deposits held with a single authorised bank.
The South African banking sector	Ring-fencing retail from investment banking is not on the cards and so protecting depositors will need to take another form, as in the rest of Europe, UK and the US. According to the SARB, the four largest South African banks (often called “the Big 4”) are Domestically Systemically Important Banks (D-SIB) and the SARB is applying many G-SIB proposals to these Big Four banks. In fact the TLAC (Total Loss Absorbing Capacity) requirement to be applied to these D-SIBs has been finalised but remains confidential. South African banks - Are dependent on wholesale, short-term funding - Tap into a retail deposit base that is small since most retail savings is channelled into pension, insurance and money market funds - Tap into a larger corporate deposit base but corporate deposits are typically “haircutted” relative to retail deposits and are therefore not as favourably treated for purposes of meeting the LCR and NSFR requirements - Have limited access to long-term funding in capital markets SA banks have - an LCR shortfall of c. R140bn (SARB; Guidance Note 6; 2013). The LCR of five of six South African banks tested by the IMF is below 100% but as the IMF noted, the SARB introduced a CLF (committed liquidity facility) which allowed all banks to meet the LCR. - An NSFR of below 100% with the exception of one bank Barclays estimates that the five largest South African banks may need to issue approximately R402bn of listed debt vs their current outstanding debt of R207bn or a total listed bank debt of R280bn. The cost of raising this debt is likely to be penal, especially if only existing instruments are used. Covered bonds may be one way of softening the blow on the local banks however, given South Africa’s lack of a scheme protecting depositors, covered bonds could subordinate the very people that the new banking regulations are seeking to protect. On the other hand, complying with the LCR and NSFR regulations is going to have an adverse impact on the profitability of local banks. No doubt the brunt of this pain this will ultimately be felt on an already stretched South African consumer, many with high debt burdens. It is therefore in the country’s interests to find a solution to this problem.
What are covered bonds?	They are senior, bullet instruments of an issuer (typically a bank) where the bond holder has recourse both to the issuer as well as an underlying collateral pool. The bullet payment can be “hard” i.e. no possibility of extension risk or is can be “soft” with the some option for the issuer to extend the repayment date by a period, typically no more than 12 months. Bonds can either be fixed rate or floating but the majority of issuance globally to date has been fixed rate. Hard bullets tend to dominate over soft bullets with mortgages being the overwhelmingly dominant type of collateral.
What collateral is typical?	Typically collateral types will be defined by legislation. In the UK, for example, where legislation was, for a long time, lacking then collateral types would have been defined in transaction documents Typical collateral types - Mortgages (residential and commercial) - Public sector exposures - Shipping, SME and aircraft exposures Mortgages account for c. 80% of collateral across global covered bond market and public sector exposures c. 18%.
What risk is there to a covered bondholder?	The bondholder is exposed to the risk of the issuer (the bank) defaulting. In the event that the bank defaults, the bondholder will have recourse to ring-fenced collateral pool, the nature of which has been pre-agreed with the bank. On bank default the credit risk changes from being bank credit risk to credit risk exposure to the underlying collateral pool. Should the collateral pool be insufficient to meet the claims of covered bondholders, then the outstanding amount becomes a claim against the issuer (the bank) and ranks pari passu with other senior, unsecured bondholders.
Why covered bonds differ from Residential Mortgage Backed Securities?	RMBS are characterised by - A pass-through structure vs a bullet under a covered bonds - Recourse to the collateral pool only vs covered bonds having recourse to both the issuer and the collateral pool - Credit enhancement in the form of subordination vs covered bonds where the enhancement is typically only through over-collateralisation
What are the structural forms of covered bonds?	There are three structural forms – the first two are typically called “legislative” models and the third a “structured” model. This terminology arises because some countries did not have covered bond legislation in place and so sought to make use of existing securitisation legislation and methods to create the economic equivalent of covered bonds. Many of these countries have since created the appropriate legislation blurring the lines between “legislative” and “structured” markets. 1. On-balance sheet Covered bond is issued from the balance sheet of an originating bank with the collateral pool remaining with the originator but ring-fenced in case of insolvency. 2. Specialist Bank Principle The bank that wishes to access covered bond financing establishes a wholly-owned subsidiary to which it transfers the collateral pool. The subsidiary issues the covered bond which is backed by the transferred collateral pool. 3. Structured/guarantor model Covered bonds are issued by the bank as unsecured obligations. Money raised by issuing the bonds is then lent to a limited liability SPV which then acquires the collateral pool using the loan proceeds. The SPV then guarantees the unsecured bonds and will use the collateral to pay bondholders in the event of the issuer’s insolvency.
Where might covered bonds feature in SA fixed income mandates?	Covered bonds rank right at the top of an issuing bank’s capital structure – above uninsured depositors (in the case of SA, that is all depositors!) and senior, unsecured bondholders. In a South African context, given the D-SIB recognition by the SARB, then the credit risk related to covered bonds (should they come into existence) issued by the Big 4 banks may lie just below that of the debt of State Owned Companies A ranking of credit risk in the SA market may look (from highest to lowest) as follows - South African government bonds - Government guaranteed bonds issued by State-owned Companies - Non-government guaranteed senior debt issued by State Owned Companies - Covered bonds issued by Big 4 SA banks - Senior, unsecured Big 4 bank debt - Other bank debt/ Corporate debt / Old-style bank sub-debt / new style bank sub-debt It is therefore very likely that fixed income managers are likely to view covered bonds somewhere between non-government guaranteed bonds issued by State Owned Companies and senior, unsecured Big 4 bank debt when looking for yield enhancement in fixed income mandates managed against both market and liability benchmarks. Assuming that SA Big 4 banks do ultimately issue covered bonds, then depending on the level of spreads on offer, the potential danger for SOC issuers is that covered bonds may be seen to offer a more attractive risk-adjusted return that their non-government guaranteed debt especially given the D-SIB status of the Big 4 SA banks. Certainly, in the early stages of any such market developing the natural process of price-discovery in the early days may create opportunities for those investors whose mandates are flexible enough to allow their fund managers room to invest in covered bonds.
Key market statistics globally	1. Largest covered bond market is Germany followed by Denmark, Spain, France and Sweden. 2. 2/3rds of covered bonds are denominated in Euros. 3. Bank in Spain and Denmark are the largest issuers of mortgage-backed covered bonds. 4. German banks are the largest issuers of public-sector backed covered bonds.

2015 - are we there yet?

2015-01-05T02:47:00.000-08:00

Defined benefit pension funds that are financially unsound will have less room to manoeuvre following recent proposals ( FSB Draft Notice on Financial Soundness of SA Pension Funds ) by the South African pension fund regulator, the Financial Services Board (FSB). The key proposals by the regulator will see:

Greater prescription of calculations used to determine a pension fund's financial soundness. In particular, an obligation on actuaries, who carry out these financial soundness assessments, to estimate a value for the pension fund's future obligations using a "market-consistent" approach. In other words, forcing a change from a "long-term" actuarial value based on an actuary's assumptions about the future to the use of a more transparent and readily observable "market-related" value for calculating the value of future obligations of the pension fund.

Any pension fund with a funding level (or ratio of assets to liabilities) that is less than 95% being required to submit, to the FSB, a clear plan for restoring its funding level to at least 100% within a period not exceeding 3 years.

Pension funds should ensure they understand the implications of these proposals and the clear impact on their investment strategies. Not to do so could lead to the adoption of inappropriate measures to counter the adverse impact of the proposed regulations. This, in turn, could condemn their pensioners to a lifetime of pension payments that are lower than they might otherwise have been.

1. Calculating the financial soundness of a pension fund requires an assessment of both asset and liability values.

Assessments of financial soundness of South African pension funds are typically carried out by a qualified actuary who will also be a Fellow member of the Actuarial Society of South Africa and be recognised as having appropriate experience to carry out such valuations.

Assessing a pension fund's financial status requires two key inputs:

1. an assessment of the value of the pension fund's assets and

2. an assessment of the value of the pension fund's liabilities.

By comparing the value of assets to the value of the liabilities we can quickly determine whether the pension fund is financial sound or not. If the value of assets exceeds the value of the liabilities then the pension fund may be considered to be financially sound.

2. Asset values are relatively easy to calculate....

Placing a value on the assets is relatively easy - a good starting point is the market value of the assets. Market values are readily available for assets that are listed on a recognised exchange, such as the Johannesburg Stock Exchange (JSE). Placing a value on assets that are not listed on exchange, for example property, is a little more difficult but even here qualified valuation agents exist and these agents are able to estimate the value using, for example, data for recent purchase (or sale) transactions carried out in similar assets. This is similar to a home-buyer looking at recent property sales for comparable properties to gauge the price for a property she may be interested in acquiring.

3. Liability values are an estimate of how much money we need today to pay all future obligations to pensioners...

The liabilities of a pension fund are its future obligations to pay pension benefits. It is, therefore, the amount of money that the pension fund must set aside today to be certain of meeting all of its future obligations to pensioners.

4. ...and this amount varies depending on what return can be earned on the money that has been set aside.

If we agree that the value of the pension fund's liability is the amount of money it must set aside today to meet its future obligations to pensioners then this amount (and hence our estimate of the pension fund's liability) will vary depending on what we estimate we may be able to earn on these assets. Intuitively this can be seen as follows: if future returns that can be earned on monies set aside to pay future pension are lower, then more money must be set aside today to be sure of being able to meet all future obligations. To calculate the value of the liabilities we therefore need to estimate a future long-term rate of investment return.

5. Actuaries have used a specific method to derive their estimate for future investment returns....

In estimating future investment returns in the past, actuaries have relied on long-term historical returns to guide them on what an appropriate assumption may be for future returns from different asset classes. So, an actuary would:

a) consider the asset classes (for example, equities, bonds, property etc) that the pension fund intends to invest the monies it has set aside to pay pensions;

b) derive an average rate of investment return on that portfolio from looking at historical returns (potentially with some adjustment for mean reversion of returns) and

c) use this average rate to estimate how much of money the pension fund should set aside to pay future pensions.

6. ...and this method means that two actuaries can place a different value on the same set of pensioner liabilities...

Under this approach, the value of the pension fund's liability varies depending on the investment mix of the pension fund. For example, if equities are assumed to return 15% p.a. over the long-term and property 12% p.a. over the long-term then a pension fund that invests 75% of its assets in equities and 25% in property will expect to earn a higher investment return than a pension fund that invests 75% in property and 25% in equities. The former will therefore believe that it can set aside a smaller amount of money (in other words there is a smaller liability value) to meet its future obligations than the latter.

Also under this approach, the the value of the pension fund's liability varies depending on the actuaries assumption for future investment returns. For example, if an actuary assumes that equities will return 15% p.a. over the long-term while another assumes that equities will earn 20% p.a. then the latter will believe that he can set aside a smaller amount of money (in other words there is a smaller liability value) to meet its future obligations than the former.

In summary, an actuary's estimate of the liability value varies depending on:

their assumed mix of assets and
their assumed rate of return for each asset type in that mix.

7. ...but can the same commitment to pay the same set of pension cashflows have different values simply based on how assets are invested or assumed rates of return for those assets?

Under the above approach a pension fund could reduce the value of its liabilities by switching its investments into higher yielding assets. For example, if a pension fund actuary valued its liabilities at R1m on a specific day based on an investment strategy that was 75% in property and 25% in equities, he could simply change the investment strategy to a higher yielding one (say 75% equities and 25% property) and reduce that liability to, say, R0.8m. Alternatively, the actuary could assume that returns on his existing portfolio were going to be higher in the future and similarly he could decrease the liability value.

What this illustrates is that estimates of pension fund liabilities can vary dramatically depending on assumptions made about investment strategy and future investment returns on a wide range of asset classes. But is this correct?

8. This method of valuing liabilities ignores the fact that investment markets already offer us an estimate of the cost of meeting this liability...

At first glance the above approach sounds sensible. It is only when one is told that an existing investment market, the bond market, already provides an estimate for the cost of meeting this liability that we begin to question whether it is fair for actuaries to place their own value on this liability -an "actuarial" value that is different to the one that the market places on this liability.

This leads to the nub of the trickery that the actuarial profession has for a long time being deluding itself with. The very same liability or commitment to pay pensions can have two very different values depending on how the assets are invested or what the actuary assumes those assets will yield. This "actuarial" view of the world was, for the most part, kicked into touch by a seminal paper ( The Financial Theory of Defined Benefit Pension Schemes - Jon Exley, Shyam Mehta and Andrew Smith ) in 1997 by three British actuaries Jon Exley, Shyam Mehta and Andrew Smith who dared to challenge the conventional view at the time.

In essence, Exley, Mehta and Smith recognised that a pension fund's commitment to pay pensions is very similar to an investment that already exists and is traded and priced by investors, namely bonds. Like our payments to a pensioner which are for a specific amount, at a specific point in time, a bond is a promise to pay defined cashflows at specific points in time. By purchasing a bond an investor will receive his investment in that bond back with interest. That interest is the return on the bond.

The payments to the bond investor will take the form of a future stream of defined cash amounts to be paid at specific points time. Included in these amounts will be both the return of the initial capital plus an amount of interest (the return earned on the capital invested). The price that an investor in this bond is prepared to pay to secure the cashflows from that bond reflects an estimate, in today's money terms, of all those future cashflows. This price (or value) provides us with information about the price (or value) that we too could use in order to place a value on our future stream of cashflows to be paid to pensioners.

To price (or value) our pensioner cashflows in a way that is inconsistent with the way in which the market prices cashflows to be paid on a bond would be to assume that we have more information and greater insight into the course of future investment returns than investment markets themselves. We should be loathe to ignore this information as it captures the expectations of millions of investors and (much like the value placed by the markets on our assets) it is a value placed by the markets on a stream of cashflows, a stream of cashflows that is very similar to our pensioner obligations.

In summary then, when working out how much money to set aside in order to meet a pension fund's commitments to its pensioners to pay a future stream of cashflows, the basic building block for an actuary's assumption about future investment returns should be the return that could be earned on a bond whose cashflows were similar to our pensioner cashflow commitments.

9. ...and by ensuring that pension fund's use this information about investment returns, from the bond market, to estimate their liabilities would not only ensure consistency amongst actuaries but also ensure that pension fund's liability estimates were consistent with the market's price for those liabilities.

Following the publication and debate of Exley, Mehta and Smith's paper in 1997 the UK pension fund industry, led by the actuarial profession, rapidly moved away from valuing their liabilities using investment returns that were inconsistent with those implied by the bond market. By doing so the actuarial profession would ensure that two different actuaries, valuing the same set of liabilities, would achieve consistency in their valuation and would no longer arrive at very different valuations because each was assuming a different rate of investment return based on either different investment policies or different assumptions for future investment returns.

Furthermore, that the estimate of those liability cashflows would also be continuously updated as new information about future bond market investment returns was received. This occurs because as bond prices change so too does the implied future investment return on those bonds. And as this investment return changes, our pension fund's estimate of its liability value should also change to reflect the market's revised view of the value of that set of cashflows.

10. But what about the fact that we could earn a higher return if we invested our pension fund money in assets which yielded more than bonds?

When we value our pensioner cashflows by looking at the value that the bond market would place on those cashflows we are also implicitly assuming that any monies set aside by our pension fund to meet its future obligations to pensioners will only ever earn a rate of return implied by the bond market. Historically, over the longer-term, bonds have tended to deliver lower yields than equities.

So it could still be argued that as our pension funds do not invest all their assets in bonds then the the future investment returns that our pension fund's can expect to earn will be higher than those implied by bond market returns. If this is the case then by solely relying on the bond markets for placing a value on how much of money to set aside to meet our future pensioner cashflows we could be adopting an unnecessarily cautious view of the amount of money we need to set aside.

That is true, however, Exley, Mehta and Smith argued that this should not change the fact that the starting point for future investment returns to be assumed by actuaries should be those implied by the bond market. It is then up to each actuary to determine how much additional return they believe that their pension fund's investment strategy could earn over and above bond returns and this could then be used to justify setting aside a smaller amount of money in anticipation of higher returns than those offered by bonds.

Again, at first glance, this may seem like no change at all.

11. We can still allow for higher returns if we take care to separate this allowance from the "market price" of our liabilities so that we are transparent about the quantum with which we are reducing our estimate of the liability due to the expectation of higher returns (than bonds) in the future.

Under the proposed approach by Exley, Mehta and Smith (and now the FSB) our assumption for future investment returns is made up of:

the return on bonds as provided by the market plus
an additional return from pursuing a riskier investment strategy as estimated by the actuary.

The difference to the past approach adopted by actuaries is that this process creates transparency and a clear attribution of the liability calculation - in particular, that part of the liability value that is a market related assessment and that part that is attributable to actuarial assumptions about future expected returns and that cannot be quantified with any certainty. The first component provides us with a way of directly linking the value of our pension fund liabilities to the investment markets price for a similar stream of cashflows. The second component explicitly quantifies by how much the trustees wish to reduce the quantum of assets they wish to set aside to meet their future pension obligations because they believe that their investment strategy offers them the scope to earn greater returns than the return on a portfolio of bonds - for the first time there is a separation of the market price for the liability value and that part of the calculation which is down to "actuarial judgement".

Now, unlike before, as bond prices (and hence expected returns in bond markets) change so too would our assumed investment return. This would mean that our liability value was being continously updated for new market information about the price of a future stream of defined cashflows.

Importantly, in deriving the second component using a carefully thought through assessment of the additional return above bonds that the actuary felt the investment strategy could earn over the longer-term, it would be very difficult for the actuary to justify a change in his assumption without sound justification and reasoning.

We can illustrate the above point with an example, Let us assume that an actuary decides that as a result of pursuing a higher yielding investment strategy he can use an additional return of 4% above the return on bonds being used to value the pension fund's liabilities. This figure of 4% would be arrived at after careful consideration of the long-term additional returns offered by other asset classes relative to the bonds being used to value of the pension fund's liabilities. This is an important point and deserves further consideration.

By studying long-term returns on bonds being used to value the liabilities we will naturally consider a variety of market cycles; both when bond yields are low and and high.

By expressing our "additional return" assumption relative to bond returns achieved over different market cycles we are recognising that whilst the absolute level of yields on different investments can vary depending on the market cycle, the return of different asset types relative to each other should display far greater stability. We may well expect that in times when interest rates are high for bond yields to be high and vice versa but the spread of returns of other assets (equities, property etc) relative to those higher (or lower) bond yields can be expected to be more stable.

We would therefore not expect this spread to change as a result of movements in bond yields, except of course if bond yields changed very dramatically so as to call into question the long-term analysis previously carried out.

Returning to our example of using a 4% additional return to assess our liabilities, then

If, at a point in time, the bond returns we were using to value our liabilities were 9% p.a., the investment return assumption used to value the liabilities would be 9% + 4% = 13%.
If, one year later, those bond yields fall to 8%, then mechanically the new process, for setting the investment assumption to be used to value the pensioner liabilities, would result in the return assumption falling to 8% + 4% = 12% from 13%.
The assumption that we can now only earn 12% (rather than 13%) would see the value that we place on our liabilities increase.
Importantly, we can now clearly see that the increase in liability value is due to a change in the market's assessment of the present value of those cashflows but our assessment of the additional return we expect our adopted strategy to deliver has not changed.
The actuary could of course change his assumption of the additional return to be earned from 4% to 5% to compensate for this change but it is debatable to what extent he could justify that long-term analysis that he previously did was not invalidated by a fall in yields of just 1% p.a.

In summary:

Liability values are split in two parts - i) the value placed on them by the bond market (in our example above, this is assessed through the 9% return assumption) and ii) the amount that the actuary assumes will be delivered by higher investment returns than the bond market can deliver ( this component of the liability is assessed through the 4% additional return assumption).
The assumed return used in the first component is transparent and can be quickly and easily assessed by looking at bond market returns which are freely and readily available.
The assumed additional return used in the second component of the liability valuation is based on the pension fund's asset mix and assumed returns for different asset types. But this assumption should be rigourously set and would not be expected to change frequently.
Liability values will now mechanically change in response to changes in bond market returns and also changes in the asset mix as well as investment return assumptions however any changes due to the latter will, unlike before, be transparent and should be justified.

11. Are we there yet? Yes, it seems like starting in 2015, with the FSB proposals we will see the uniform adoption of the above principles to valuing pension fund liabilities by SA pension funds...

South African pension funds have been far slower to fully adopt all the principles espoused by Exley, Mehta and Smith.

Almost 18 years post the publication of Exley, Mehta and Smith's paper it seems like 2015 may be the year finally achieve uniformity and consistency and that with the FSB's latest proposal the stage is now set for South African pension funds to embark on the road to convergence where all pension fund liabilities will be valued in a way that is consistent with pricing implied by the bond market for those cashflows and for any margins due to higher expected investment returns to be separately quantified and justified.

12. ...creating more volatility in a typical pension fund's financial position...

A pension fund's financial soundness assessment must be explicitly related to bond market returns. These returns change daily. As we have shown above, if these bond yields fall then the liability value will increase. An increase in the liability value (without a corresponding increase in the value of assets) will result in the financial position of the pension fund deteriorating. Again, this makes sense to us in that if future returns that can be earned on monies set aside to pay future pension are lower, then more money must be set aside today to be sure of being able to meet all future obligations.

In particular, the FSB's proposals call for the investment return assumption to be set in a way similar to that proposed by Exley et al. The proposal goes further and expects any assumption used to be realistic and guided by past experience or reasonable expectation of changes in the future.

Now, as bond market yields change so too will the liability value of the pension fund.

13. ...and more volatility for the companies sponsoring those pension funds...

This greater volatility should be seen together with the further requirement that where the funding position declines to below 95% then the pension fund trustees must submit a plan to the FSB for restoring the funding level to at least 100% within 3 years.

There are only two ways to restore the funding level:

- to take more investment risk in search of higher returns on the money that has already been set aside or

- for the sponsoring company to commit to setting aside more money.

The second option is likely to be unpalatable to many companies since it introduces uncertainty into their financial planning - an uncertainty which is linked to the vagaries of investment markets that are mostly beyond their control.

14. ...leading to a greater need to consider measures to mitigate the impact of the FSB's proposal.

One consequence will be that sponsors are increasingly likely to find that their existing investment strategies will not help protect them against volatile liability values which in turn could require them to cough up more money for their pension funds, potentially to the detriment of other business opportunities.

Investment strategies that offer protection against volatile liability values will be those that invest in assets whose value rises in response to declines in bond returns. These strategies are know as Liability Driven Investment (LDI) strategies.

But there is no free lunch; unfortunately LDI strategies can be lower yielding but the good news is that they need not be.

15. Pension funds should act but also be careful not to throw out the baby with the bath water...

South African pension funds find themselves in an enviable position of having healthy funding levels, Trustees would be wise to move now to protect those positions but should do so judiciously and without panic. Moving to a low yielding LDI strategy without considering the alternatives could jeopardise all the good work of the past few years and condemn pensioners to years of poor pensions -with no prospect of inflation-beating increases.

But this need not be the case. UK pension funds have led the way by implementing LDI strategies without compromising on their ability to pursue higher returns. South African pension funds would be wise to do the same. Before committing to a low-yielding LDI strategy, trustees and their advisers simply must explore opportunities for yield enhancement.

But in doing so they should consider whether the LDI manager they are talking to is best placed to deal with another bit of regulatory action on the horizon - the FSB's draft proposal on regulating many of the investment instruments used by investment managers offering LDI strategies. That however is a subject for an entirely different blog.

Note: some pension funds have already embraced the principles of Exley et al and these should see little change as a result of the FSB's proposals.

What do Tesco and tracker funds have in common?

2014-10-28T04:01:00.001-07:00

For a start they are both liked by Warren Buffet. Like Warren I like both of these investments. Like Warren, I am smarting from my losses in Tesco. Tesco has been a painful lesson.but I don't believe that it negates the advice to be long tracker funds.

In his annual letter to Berkshire shareholders, Warren Buffet says:

My money, I should add, is where my mouth is: What I advise here is essentially identical to certain
instructions I’ve laid out in my will. My advice to the trustee could not be more simple: Put 10% of the cash in short-term government bonds and 90% in a very low-cost S&P500 index fund. (I suggest Vanguard’s.) I believe the trust’s long-term results from this policy will be superior to those attained by most investors – whether pension funds, institutions or individuals – who employ high-fee
managers.

Just prior to making this point, he notes:

I have good news for these non-professionals. The typical investor doesn’t need this skill. In aggregate, American business has done wonderfully over time and will continue to do so (though, most assuredly, in unpredictable fits and starts). In the 20th Century, the Dow Jones Industrials index advanced from 66 to 11,497, paying a rising stream of dividends to boot. The 21st Century will witness further gains, almost certain to be substantial.

The goal of the non-professional should not be to pick winners – neither he nor his “helpers” can do that – but should rather be to own a cross-section of businesses that in aggregate are bound to do well. A low-cost S&P 500 index fund will achieve this goal. That’s the “what” of investing for the non-professional.

The “when” is also important. The main danger is that the timid or beginning investor will enter the market at a time of extreme exuberance and then become disillusioned when paper losses occur. (Remember the late Barton Biggs’ observation: “A bull market is like sex. It feels best just before it ends.”) The antidote to that kind of mistiming is for an investor to accumulate shares over a long period and never to sell when the news is bad and stocks are well off their highs. Following those rules, the “know-nothing” investor who both diversifies and keeps his costs minimal is virtually certain to get satisfactory results. Indeed, the unsophisticated investor who is realistic about his shortcomings is likely to obtain better long term results than the knowledgeable professional who is blind to even a single weakness.

If “investors” frenetically bought and sold farmland to each other, neither the yields nor prices of their crops would be increased. The only consequence of such behavior would be decreases in the overall earnings realized by the farm-owning population because of the substantial costs it would incur as it sought advice and switched properties. Nevertheless, both individuals and institutions will constantly be urged to be active by those who profit from giving advice or effecting transactions. The resulting frictional costs can be huge and, for investors in aggregate, devoid of benefit.

So ignore the chatter, keep your costs minimal, and invest in stocks as you would in a farm.

My observations:

- I am hanging on to my holdings in Tesco. At 400p, Tesco's properties were worth as much as the then market capitalisation of the company. At 170p, this is not the time to blink but to focus on the tangible value in this business. It will take a long time for a competitor to build a retail footprint as dominant as Tesco's position in the UK. Tesco needs to get back to basics and focus on what made it successful - the right products at the right price. Asda has stolen this position from Tesco and Tesco need to claim back that position.

- UK plc is fundamentally still overvalued compared to US Inc. The US economy is a giant when it comes to being flexible as being able to reinvest itself. The UK economy has fewer weapons in its armoury. Don't fight the US - owning a broad cross section of US companies at the current time is probably the best asset allocation decision one could make today. Doing this via trackers funds is probably the second best investment decision one could make.

- Not all trackers fund are created equally, make sure you shop around. When Vanguard launched their trackers funds in the UK the incumbents were forced to drop their asset management fees. The barriers to entry to the 'low-cost' tracker business are high. Choose a tracker fund that is managed by one of the largest providers and you should generally benefit from the best fees as well as low total expense ratios. Globally the top 3 managers would be: BlackRock, State Street and Vanguard - the latter is also liked by Warren.

Using LDI to set pension fund investment strategy

2014-10-27T11:03:00.001-07:00

What is LDI?

LDI is a common acronym for Liability Driven Investment. Globally, corporate-sponsored, defined benefit pension funds have embraced an LDI-framework to setting investment strategy. Adopting an LDI strategy means that investment risk is no longer defined predominantly in relation to the standard deviation of investment returns. Rather, the concept of investment risk is extended to include all risk which could impact the volatility of asset values relative to liability values. In summary, LDI is a framework for setting investment strategy which has, as its end goal, the reduction of volatility in asset values relative to liability values.

Importantly LDI is not limited to consideration of a single asset class, for example, bonds; the LDI framework can (and should) be applied across all asset classes in order to arrive at a holistic view of the volatility of all asset classes relative to the liabilities. The table and diagram below sets out the types of risk that will typically be considered when adopting an LDI approach.

Table 1: Factors influencing asset and liability values

Factors influencing volatility of asset values	Factors influencing volatility of liability values
Systematic (or market) risk (e.g. equity market risk for equities or property market risk for property investments	Interest rate risk
Credit risk	Inflation risk
Liquidity risk	Longevity risk
Active manager risk	Model risk

Chart 1: Risk attribution with liabilities

Table 2: Brief description of factors influencing asset and liability values

Factors influencing volatility of asset and liability values	Description
Systematic (or market) risk (e.g. equity market risk for equities or property market risk for property investments	This is risk which characterises the asset class to which an investor is exposed. It cannot be diversified by holding a diversified portfolio of securities within the asset class.
Credit risk	The risk of losing some or all of your investment due to deterioration in the credit-worthiness of your counterparty. While it can be included as part of market risk, for some asset classes, such as corporate bonds, it can make sense to measure this separately due to its importance to the overall risk associated with the asset class. Some quantification of this risk can be made by observing historical, long-term average default rates.
Liquidity risk	The speed and cost at which an investment can be sold for cash.
Active manager risk	The risk that an active manager underperforms its benchmark. When considering its strategic asset allocation, a fund may assume that it can achieve certain market returns, however taking a decision to appoint an active manager means that the manager can underperform the market.
Interest rate risk	The risk that the value of the assets (or liabilities) changes in response to changes in interest rates. Fixed rate and inflation-linked bonds will typically be the highest contributors towards an asset portfolio’s interest rate risk. Liability cashflows are valued to today’s monetary terms by discounting them to the present day. An increase in the rate of interest used to discount these liabilities will decrease their present value and vice versa. An often quoted, but narrower definition of LDI, is that LDI is about constructing an investment strategy that better aligns the interest rate sensitivity of the assets to those of the liabilities.
Inflation risk	The risk that the value of the assets (or liabilities) changes in response to changes in the market’s expectations for future rates of inflation. Inflation-linked bonds will typically be the highest contributors towards an asset portfolio’s inflation risk. Liability cashflows are valued to today’s monetary terms by discounting them to the present day. Where future cashflows to be paid increase in line with inflation then an increase in expectations for future rates of inflation will increase the present value of these cashflows and vice versa. An often quoted, but narrower definition of LDI, is that LDI is about constructing an investment strategy that better aligns the inflation sensitivity of the assets to those of the liabilities.
Longevity risk	This is the risk that the liability cashflows to be paid in the future increases as a result of the recipients of those cashflows living longer than anticipated. Generally, deteriorating longevity (pensioner dying earlier than expected) is considered to be less of an issue as it does not lead to a strain on the finances of the pension fund.
Model risk	This is the risk that the actuary’s projection of future cashflows turns out to be lower than those cashflows that actually materialise. Key demographic assumptions are sometimes embedded in these cashflow estimates, for example, assumptions relating to spouses pensions and early or ill-health retirement experience. The pension fund’s actual experience is likely to differ from this.

What have been the catalysts for this focus on LDI?

Globally we can point to two main catalysts for extending the definition of risk and hence for the focus on LDI:

1. Accounting regulations:

During the noughties changes in global accounting regulations resulted in sponsor balance sheets becoming more responsive to recognising changes in pension fund surpluses or deficits (defined as the difference between assets and liabilities) and the latter moving to the use of mark-to-market approaches to value both the assets and liabilities. Together these developments introduced greater volatility in a sponsor’s balance sheet as a result of changes in the pension fund’s surplus or deficit. CFO’s became increasingly concerned that such volatility could materially (and adversely) impact the company’s financial results for reasons that were unrelated to the company’s core business. This was especially true for companies that had pension fund’s whose liabilities were large relative to the company’s market capitalisation – a small, adverse change in a large pension fund liability could swamp any positive performance in the company’s core business.

The poster-children for the adverse impact that these developments could have on a company were two well-known British companies, British Airways and British Telecom. Table 3 below illustrates that the relative size of the pension liabilities and market capitalisations at these two companies are such that a small change in their liability values would have a large impact on their market capitalisation.

Table 3: Pension liabilities and deficits (Source: Accounting for Pensions 2010; LCP)

Company	Pension liabilities	Pension deficit	Market capitalisation	Liabilities / Market cap	Deficit / Market cap
British Airways	£12.8bn	£0.6bn	£1.6bn	791%	37%
British Telecom	£33.2bn	£4bn	£6bn	551%	66%

2. Funding regulations:

Following a number of high profile pension fund failures; the result of failure of the sponsor coupled with a poorly funded pension fund, many pensioners were left stranded. Regulators moved to tighten funding regimes for pension funds by emphasising the need for a clear plan to deal with any pension fund deficit and within an acceptable timeframe. Increasingly, the emergence of deficits in pension funds were at the top of the agenda for company financial managers who could no longer kick the can further down the road. Deficits were having a direct and adverse impact on their cashflows by requiring higher contributions to be injected into the pension fund. All of this was taking place in the midst of the worst financial crisis in history.

In the early part of the noughties these themes played out across Continental Europe and the UK and then, in the latter part of that decade, these changes played out in the US and closer to home in South Africa. Today, corporate pension plans in the US and South Africa are leading the charge to the adoption of LDI strategies with many sponsors in these countries also moving to the “so-called” end-game more swiftly than their UK counterparts, i.e. the transfer of these pension liabilities to an insurance company and, in so doing, entirely eliminating the pension liability from their company balance sheets. But that is a subject for a future article.

LDI in Africa

Defined benefit pension schemes are not as prevalent in Africa as they are in the UK and the US. In South Africa, the continent’s largest pension fund market, defined benefit pension schemes were closed to new entrants in the nineties and their demise was hastened by offering members incentives to transfer out to newly established, defined contribution arrangements. That said, a few large legacy defined benefit pension funds are still in existence and many of these funds have moved towards adopting a LDI strategy, especially for assets backing their pensioner liabilities. The adoption of similar strategies by large pension funds in the rest of the continent is not without challenges due to less developed capital markets but, as discussed, LDI is not about investing in specific assets but rather about the adoption of a framework for the holistic consideration of managing funding level volatility. Adopting such a framework is of paramount importance as a first step towards improved risk management and governance.

Future developments

An LDI framework is not limited in its application and can (and should) be extended to defined contribution arrangements. The absence of a clearly defined or guaranteed liability does not imply the absence of such a liability. In fact, members of defined contribution pension funds are left with no different a challenge – they too need to be sure that they are accumulating sufficient savings in their pension funds on which to ensure a comfortable existence in old age. Arguably, members of these defined contribution arrangements require even greater financial assistance to define clear retirement objectives and goals. LDI can be used to frame such goals by linking savings goals to incomes required in retirement and in this way setting an appropriate long-term investment strategy.

In summary, LDI is a framework for managing investment risk. It has proved to be a useful tool for setting investment strategy especially where there are dual objectives of targeting a long-term savings goal whilst still focusing on the need to ensure acceptable investment outcomes in the short term. There is an analogy to be drawn between a limited-over cricket game and LDI. In both cases the long-term goal is achieved by the successful achievement of a series of shorter-term goals which when taken together yield the desired end result.

Guns don’t kill people, people kill people: lessons in money-lending from African Bank

2014-08-21T14:38:00.002-07:00

1. Introduction

Events here in South Africa, over the last two weeks, have left me with that feeling of déjà vu. I am referring of course to local lender African Bank being taken into curatorship following an announcement by the governor of the local central bank on Sunday, August 10^th. The week before African Bank’s share price nosedived in response to the unsecured lender’s announcement that its CEO had quit and that the unsecured lender had suffered a further deterioration in financial performance. The bank would need to come to market to raise yet more capital; despite previous reassurances to the contrary. Remembering the events which played out in September of 2008 when Lehman collapsed, I remarked to a colleague prior to that fateful weekend, that I couldn’t see how African Bank would last the weekend.

And then it came. Like Lehman, which was forced to file for Chapter 11 bankruptcy on a Sunday, the local central bank duly made an announcement on the Sunday, although, unlike Lehman, the central bank chose to step in and (mostly) rescue debt holders. Equity holders were not so lucky; six years, 1 month and 4 days earlier, nor was Dick Fuld¹.

Lehman employees at the Canary Wharf office in London receive the news of the bank's Chapter 11 filing on Monday morning September 15th 2008.

The now infamous report by Morgan Stanley analysts which called on equity investors to reload on Lehman stock only months before its collapse.

While I think it is fair to say that 2008 was the year of reckoning for many institutional and retail investors globally, South African investors were, for the large part buffeted from those events. The events of financial distress at that time were many and varied but the poster-child for the period is no doubt the default of the investment bank Lehman¹. The crisis in essence stemmed from the fact that banks lent too much money to people who were unable to repay their debt. That South African banks emerged mostly unscathed was a source of great pride and the local financial system was held up as a shining example of a well-regulated industry with sensible lending practices. And then over the last 6 years as scandal after scandal has emerged - Madoff², LIBOR-fixing³, dark pools⁴, high frequency trading⁵, commodity price fixing⁶ and so the list goes on, I daresay that South African's felt slightly smug.

Bernie Maddoff in happier times

Any delusions of grandeur South Africans may have had about superiority in the appropriate regulation and safeguarding of the financial system were laid to rest with the effective collapse of African Bank and the domino effect that event has had on our banking, savings and investment industries. While, the large South African banks will still feel a certain amount of smugness, the failure of African Bank is, arguably, also an indictment on the lack of a sufficiently responsive feedback loop between regulators and the private sector so that perhaps the latter must shoulder some responsibility?

2. Lessons from African Bank

Lesson 1: "Credit worthiness is like virginity; it can be preserved but not restored very easily"

Like their global counterparts in 2008, South African pension funds, insurers and individual investors have had 'golden circle' tickets to the greatest show in town. If I were to attempt to distil all the lessons learnt over the last few weeks then much like in 2008 it would be simply this: throw out everything you thought you knew to be true, assume nothing and question everything. If I were to apply this mantra to one feature of recent events it would be to consider the central role that assessments of credit worthiness have played in this latest crisis. I say this because, in the case of both Lehman and African Bank, once it became clear that their lenders had lost faith in their ability to successfully navigate their financial woes their sources of funding dried up and their fate was sealed.

In late 2013, the US government was engaged in a dangerous game of chicken with its lenders. The Treasury Department was fast reaching its borrowing limit (as set by the US Congress) and needed an act of Congress to borrow beyond this limit. Not raising the limit would have meant that the US Treasury would have been unable to borrow money to meet upcoming commitments on its maturing debt obligations. Warren Buffet noted that it would be ludicrous for the US to contemplate defaulting⁷ on its debt repayment obligations and said that "credit worthiness is like virginity; it can be preserved but not restored very easily".

The lighter side of the debate on the US debt ceiling debate

Lesson 2: To assume is to presume

If we accept Warren Buffet’s comment as being an accurate reflection of the importance of credit worthiness, then surely investors should be aiming to ensure that the tools in place for measuring credit worthiness are robust and as infallible as can be. After all, a significant deterioration of credit worthiness could trigger a downward spiral in the financial fortunes of a borrower, one that was difficult, if not impossible, to recover from, leaving the lender vulnerable to not recovering the amount they have lent.

The failure at African Bank has placed the spotlight firmly on those making these assessments, including:

equity fund managers who chose to buy shares in African Bank and so were implicitly making judgements about the safety of their client’s investments and the likelihood of having their capital returned, let alone achieving an acceptable return on their investment;
money market and fixed income fund managers who lent African Bank money by buying their bonds and
credit rating agencies (CRA’s)⁸, professional firms in the business of assessing making credit quality assessments.

Most of the public attention has fallen on the equity and money market fund managers:

equity fund managers are being criticised because they have lost substantially all of their client’s money that was invested in African Bank shares and
money market fund managers are being criticised because their money market funds were sold as ultra-safe, something that was close to cash, yet for the first time in history these funds have ‘broke the buck’ – investment speak for saying that investors in money market funds can expect to lose some of their initial investment.

But with the recent credit rating downgrade of the Big Four South African banks (ABSA/Barclays, FirstRand, Nedbank and Standard Bank), attention is turning to the credit rating agencies, professional firms whose business model is premised on making money from evaluating and then rating the credit worthiness of borrowers. Professional money managers, investment advisers and clients place their confidence and trust in these ratings and billions of Rand, Pounds and Dollars are invested by reference to these ratings.

The main criticism levelled against these purveyors of credit worthiness assessments is that they have been slow to react. If all was really well at African Bank why did these rating agencies continue to award African Bank a rating that only the most credit worthy of institutions would have deserved (known as an ‘investment grade’ rating⁹) right up to the point of African Bank going into curatorship. It seems that while referencing a credit rating can be important part of an investor’s due-diligence, it is by no means sufficient to fully assess the credit quality of a borrower.

Given what we have just said about credit worthiness, you may be forgiven for thinking that the global financial services industry would have an established, robust and commonly accepted system for consistently measuring the creditworthiness of a company. It is probably not an exaggeration to say that today, the complete opposite is true and that the most common global approach to measuring credit worthiness, a credit rating issued by a recognised credit rating agency, has embedded flaws.

So what, I hear you say? Well,

if the people you trust to manage your money place an undue reliance on these ratings then perhaps they are taking more risk with your money than is immediately apparent and
if the regulators entrusted with protecting the financial system are slow to regulate to prevent the unintended consequences from this flawed system of credit ratings then perhaps investors need to be alert to the flaws of the system and find alternate ways to manage their risk.

It would be dangerous to assume that credit ratings are a panacea for assessing credit worthiness of borrowers.

Lesson 3: Trust nothing; question everything; historical quirks have created deep ‘fault-lines’ in our financial system. It sometimes takes a seismic event for the ‘fault-line’ to be noticed. Unfortunately such seismic events rarely occur without leaving behind a trail of destruction in their wake.

One way of gauging investor confidence in the financial fortunes of a company is the company's share price. Another way is to consider that company's cost of borrowing or the additional premium demanded by the company’s lenders versus lending to the most secure of borrowers, typically seen as the government of the country. There is, however, a third, and arguably even more influential determinant of investor's perceptions of a company's credit worthiness and that is a company's credit rating.

Background: the advent of credit ratings?

Very simplistically, investors in stocks and bonds are lenders of money to the companies they invest in. Ordinary shareholders expect that in exchange for committing their capital to the company, they will be rewarded through a pro-rata share of the company's future (unknown) profits but also recognise that they are first in line to lose money should things go wrong with the company's business. On the other hand, those lending money to a company, for example, by buying their bonds (a form of tradeable loan), expect to be paid a well-defined return on their investment (a pre-specified rate of interest) and ultimately expect their loan to the company to be repaid on time.

Many professional investors have historically relied on credit rating agencies ("CRA's"). Modern day CRA's date back to 1909 when American John Moody started offering credit ratings on American railroad bonds in a book called ‘Analysis of Railroad Investments’. Until then most bond investing was in the public (or sovereign) debt of governments and investors (the bondholders) trusted those governments as being willing and able to honour their obligations. Partnoy (1999) traced the origins of the industry back to Lewis Tappan. Tappan, a 19^th century businessman in the silk industry kept detailed credit information about his customers which proved valuable to other merchants when the silk business ran into difficulties. In 1841, Tappan formed the The Mercantile Agency – the first credit rating agency (Source: BoE: Financial Stability Paper No.9-2011 Whither the credit ratings industry?)

John Moody, founder of the modern credit rating agency

But in the early 1800's the American railroads were being built by privately owned railroad companies. The large amount of capital needed to finance the building of railroads in what were then 'frontier' regions meant that railroad companies turned to issuing bonds - effectively borrowing money from US and international investors against their future expected profits. Not only was this the beginning of the corporate bond market as we know it today but the development of this market eventually led John Moody to offer a service, rating the credit quality of these borrowers. Remember this was before the establishment of the US Securities and Exchange Commission (SEC), which was created in 1934 and required public companies to issue standard financial statements.

Shortly thereafter, in 1916, the Poor company, started by Henry Varnum Poor, turned its attention from publishing the Manual of the Railroads of the United States (a compendium of financial information on the railroad companies) to rating railroad bonds. The company merged with Standard Statistics in 1941 to form Standard & Poor's (S&P). Today Moody's and S&P are the world's largest credit rating agencies.

So powerful have the rating agencies become, as the definitive voice on a borrower’s credit worthiness that Thomas Friedman, a television interviewer on PBS, would say on 13 February 1996:

"There are two superpowers in the world today in my opinion. There’s the United States and there’s Moody’s Bond Rating Service. The United States can destroy you by dropping bombs, and Moody’s can destroy you by downgrading your bonds. And believe me it’s not clear sometimes who’s more powerful."

Rating agencies are the strangest of hybrids; a seemingly split personality. Most organisations with a regulatory role are non-profit organisations. Credit rating agencies (CRA’s) on the other hand are profit-making companies performing a regulatory role¹⁰. The conflict is problematic mostly because of the manner in which the CRA’s are remunerated. When John Moody first conceived his rating agency (or manual), in exchange for selling his rating manual, Moody was remunerated by his investors who were end users of the information he provided. In the 1970s, the concept of ‘issuer pays’ began to take hold. Under ‘issuer pays’, the entity issuing bonds (borrowing money), pays the CRA to rate their bonds. There have been many reasons put forward for the change¹¹. One of the quirkiest reasons is that the advent of high-speed photocopying in the early 1970s meant that once a single investor had bought and paid for a rating, it could be easily duplicated and passed on free of charge to other investors. Whatever the reason, with the benefit of hindsight, the conflict of interest in the ‘issuer pays’ model is clearly apparent – it can be compared to a restaurant paying a food critic to evaluate the quality of the dining experience on offer. More bluntly, the question critics raise is around the temptation for a rating agency to massage up a rating in order to prevent an issuer seeking a different (possibly higher) rating from a competitor agency.

Regulators have long recognised the Jekyll and Hyde nature of rating agencies and are now setting about to change this, specifically by eliminating references to credit ratings in new regulations governing financial stability. Globally, the Financial Stability Board (FSB), chaired by the Governor of the Bank of England, Mark Carney is leading the charge. In October 2012, the FSB, of which South Africa is a member, published a roadmap for implementing its principles for reducing reliance on credit rating agency ratings¹¹. The roadmap to removing the references to credit ratings embedded in regulations is being implemented globally, including here in South Africa, and there is now widespread acknowledgement that mechanical reliance on credit ratings to ensure the credit-worthiness of an investment is no substitute for fundamental due-diligence and analysis. Furthermore, baking into regulations a reliance on credit ratings as a means for measuring financial strength may in fact be compounding the problem of mechanical reliance and fails to recognise that the credit rating business has embedded conflicts of interest.

Mark Carney appears in front of the UK Treasury Select Committee in advance of his confirmation as the first non-Brit to become Governor of the Bank of England. Carney assumed the role on 26 November 2012

What is not yet changing is the ‘issuer pays’ model and so while the split personality is changing, the conflict of interest to investors relying on these ratings will likely persist for some time. The winds of change will, to many, feel more like a breeze but what this does highlight is the potential for latent conflicts of interest in our financial system to pass undetected for long periods of time. These conflicts of interest are, in of themselves, not necessarily problematic. What is problematic is not taking being aware of the conflict when using these ratings in ones assessment of credit risk. As we will illustrate in our next point, whilst the current model may be imperfect, the rating agencies themselves are transparent on these points so it should be the role of the end-user to understand how best to use the output from the credit rating agencies.

Lesson 4: There is no such thing as a free lunch; if something looks too good to be true it probably is. A credit rating does not reflect all information about the credit worthiness of a portfolio. If regulators are changing regulations to remove references to ratings as a measure of credit worthiness perhaps investors should, at the very least, try to broaden their approach to controlling the credit quality of those they lend money to.

Rating agencies have a long history of slow reaction to financial distress. This dates back to the default of Penn-Central Railroad¹³ and First Executive Corporation but it was the defaults of US companies Enron on 28 November 2001 and WorldCom on July 21 2002 that drew the ire of US politicians who reacted angrily to the fact that the Big Three rating agencies maintained an ‘investment grade’ rating for Enron’s bonds until five days before Enron declared bankruptcy.

The lighter side of the Enron scandal

The delayed response by rating agencies was also prevalent when Lehman Brothers defaulted on Monday 15 September 2008. The Friday before (12 September), Lehman bonds held an investment grade rating. This phenomena, known as ‘jump-to-default’ – where a seemingly credit worthy institution can move, literally overnight, to being in default for many investors is seen as all too common an occurrence so perhaps we need to understand why these professional rating agencies seem unable to foresee impending trouble or have investors misunderstood the purpose of these ratings?

The key lies in recognising that credit ratings are not necessarily designed to be a forward predictor of deteriorating credit worthiness. Furthermore there are many studies in finance literature which demonstrate that a borrower’s future credit worthiness (or more aptly its lack thereof) may be better predicted by a:

- deterioration in its share price¹⁴;

- an increase in its credit spread¹⁵ (the additional yield demanded by lender’s to lend to a company rather than a safer borrower, say, the government); and

- an increase in the implied cost of insuring against a default by the borrower¹⁶;

There are many structural reasons that explain why, by design, credit rating changes may lag actual changes to a company’s likelihood of default and hence why credit ratings may not be the best predictor of future credit worthiness. We outline some of these below.

1. There is evidence to suggest that rating agencies adopt an approach known as a ‘through-the-cycle’ approach. This means that an assigned rating may not immediately change to reflect adverse events unless those adverse events are expected to be long-term and significant enough to cause a move to a lower category on their rating scale. This means that rating changes by design take place quite gradually and may not be as timely as investors may assume them to be. Rating agencies have come in for a fair degree of criticism on this point from investors demanding a more timely credit assessment. This criticism is too often levelled by those with an insufficient understanding of the credit rating agencies methodologies and their limitations.

2. Credit rating agencies use a rating scale which has a limited number of categories¹⁷. As a result it is possible that the credit worthiness of a company has deteriorated but that it has not deteriorated to a sufficient extent to warrant a downgrade to a lower rating category. [In technical jargon, credit ratings are a discrete variable whilst the likelihood of default is a continuous variable.]

3. Credit ratings are not being continuously updated yet, the likelihood of a company defaulting changes in real time. Some lag in updating the rating is therefore inevitable.

There are a few implications that arise from these observations:

- Credit ratings are not meant to be a panacea and are no substitute for a thorough bottom-up fundamental analysis.

- Exposure limits set by bond investors for their fund managers (e.g. no more than a certain percentage invested in bond instruments with a particular minimum credit quality) are but one tool for controlling credit risk. A simple mechanical rule for limiting exposure by credit-rating is unlikely to be sufficient to control for credit risk.

- It is entirely possible for, say, two fund managers to each construct a debt portfolio with the same ‘average’ credit rating, yet offering very different yields. Investors and their advisers should recognise that, all else being equal, for a given average credit rating, the differences in yield between these two portfolios should act as a flag about the true credit worthiness of the two portfolios. This ‘flag’ with respect to the credit quality would not be apparent by simply looking at an average credit rating.

- Fund managers should not be adding securities to their money market, income or other fixed income portfolios purely on the basis of the comfort they are drawing from a credit rating about the borrower’s credit worthiness

There are good reasons for the reactive nature of credit ratings and it is the responsibility of users of credit ratings to understand this, and other, shortcomings of credit ratings. Again, the mechanical use of ratings as a panacea for assessing credit quality is unfortunately far too common amongst investors, including professional investors.

Credit ratings have their shortcomings but they are still a useful tool if investors take time to understand and compensate for their limitations. Credit ratings cannot shoulder the blame for the follies of investors who use them without a thorough understanding of their limitations; much like guns credit ratings are only dangerous when used incorrectly and for the wrong purpose.

--------------------------------------------------------------------------------------------------------

Notes

(1) Dick Fuld / Lehman default

Lehman Brothers was a US investment bank that filed for bankruptcy on September 15^th 2008. DicK Fuld was CEO of Lehman at the time it filed for bankruptcy and is commonly thought to be the person whose actions, more than any other, contributed to the demise of Lehman. Lehman was the largest bankruptcy filing in US history, as measured by assets held at the time of filing for bankruptcy. Lehman had $691bn of assets at that point. Until Lehman WorldCom was the largest bankruptcy; when WorldCom filed for bankruptcy it had $104bn of assets.

Source: http://www.businessinsider.com/largest-bankruptcies-in-american-history-2011-11?op=1

(2) Madoff

Bernie Madoff ran a pyramid scheme (also known as a Ponzi scheme) out of his Manhattan office, promising investors unusually large returns, using money from new investors to pay returns to older investors. Madoff was caught in December 2008 when investors started demanding returns. Madoff’s shenanigans shocked the already bruised global financial system that was still reeling from the default of Lehman Brothers only 3 months earlier.

(3) LIBOR-fixing

LIBOR (London Interbank Offer Rate) is the average interest rate at which banks can borrow from one another. London is mentioned in its name because the benchmark is set in that city. A number of banks estimate how much interest they would pay to borrow money on a short-term basis from other institutions. While the process is still overseen by the British Bankers' Association, the calculations are now performed by Thomson Reuters. Thomson Reuters discards the four highest and four lowest submissions as outliers, and averages the remaining ones.

The data provider then publishes its calculations, generally around 11:30 a.m. London time, along with each bank's submissions. There are actually 150 different Libor rates published every day. They cover 10 currencies and 15 maturities.

Essentially, Libor is one of the main rates used to determine the borrowing costs for trillions of dollars in loans. Interest rates on some mortgages, student loans and credit card accounts rose or fall when Libor moves. Often the rates are adjusted annually or quarterly, rather than every day. Interest rate swaps may reference LIBOR if they reference a floating rate of interest.

In late June 2012 news broke of fines of close to $450m being imposed against Barclays PLC. This was the first time that the public heard of the manipulation of this key interest rate even though it would subsequently become clear that the earliest evidence of LIBOR-fixing dated back at least 4-years prior.

(4) Dark pools

Dark pools are a private trading venue where details of trades are not made available to the public. The main advantage of these trading venues is allowing their participants to conduct trading activity without revealing their true investment intentions – the fear being that if their investment intentions were made public, knowledge by the wider public could adversely impact the performance of their intended investment strategy.

As the name suggests, these private trading venues operate away from the public exchanges like the London or New York Stock Exchanges. Instead they are operated by large investment banks.

CBS News noted that: ‘The problem is that so much trading is now happening in dark pools that it may be warping publicly quoted stock prices to the extent that they no longer properly represent where the market is. For example, if a lot of sell orders for stock in ABC123 Corp. are waiting to be fulfilled in a dark pool, then buyers in the pool and elsewhere don't know that the price of that stock should be lower than it is. Further, because dark pools base their prices on the prices from the public exchanges, then the prices in the dark pools will be wrong as well.

Also, dark pools have several characteristics in common with things that contributed to the financial crisis: lack of transparency, under-regulation and, because of the amount of trades they now handle, they have clear systematic links to the rest of the financial system and the entire economy.’

(5) High frequency trading

High-frequency trading (HFT) is algorithmic trading that involves the very rapid placement of buy and/or sell orders, in the realm of tiny fractions of a second. It is alleged that HFT firms are often involved in front-running whereby the firms trade ahead of a large order to buy or sell stocks based on non-public market information about an imminent trade. Another criticism is that HFT has increased the level of potential market systemic risk whereby shocks to a small number of active HFT traders could then detrimentally affect the entire market. A related concern is whether HFT could exacerbate market volatility. These concerns have percolated since the “Flash Crash” of May 6, 2010, when the Dow Jones Industrial Average (DJIA) fell by roughly 1,000 points in intraday trading—the largest one-day decline in the history of the DJIA. The crash was analysed in an investigative report by the SEC and CFTC which, among other factors, looked at the role that HFT may have played and determined that it was not the cause, but may have exacerbated the crash.

Regulators have been scrutinizing HFT practices for years, but public concern about this form of trading intensified following the April 2014 publication of a book by author Michael Lewis.

Source: High Frequency Trading: Background, Concerns and Regulatory Developments; Gary Shorter and Rena S. Miller; 19 June 2014; Congressional Research Service. http://fas.org/sgp/crs/misc/R43608.pdf

(6) Commodity price fixing

Prices for oil and other commodities are estimates based on either:

- standardised contracts that trade transparently on regulated exchanges or

- data on bids, offers and deals as reported by unregulated price-reporting agencies (PRA’s) which make money by gathering market information and selling it to subscribers.

The European Commission and the US Commodity Futures Trading Commission (CTFC) in 2013 started investigating possible market-rigging of commodity prices by oil companies. Platts, the largest PRA was raided by the EU in 2013.

Source:TheEconomist http://www.economist.com/news/finance-and-economics/21595943european-union-wants-change-how-commodity-benchmarks-are-set-fixing-fix

(7) Has the US defaulted before?

Not really. There are three examples in US history that come close to default, with the most recent occurring in 1979. Then, the US Treasury inadvertently defaulted on $122m, because of what it said was a word processing error. Although the error was quickly fixed, and even though $122m was a tiny fraction of the $800bn in debt that the Treasury had at the time, a study found that the mini-default raised the cost of borrowing by 0.6%, or $6bn a year.

The other two instances, in 1933 and in 1790, both involve defaults akin to the current situation in Greece, when creditors were forced to take less money than what they were owed. Some economists have defined this as a default, but it's murky territory.

(Source: BBC)

(8) Credit rating agencies

A credit rating agency is a professional firm which issues assessments on bonds that provide investors with an opinion on the credit quality of the bond – i.e. an assessment of the borrower’s ability to pay both interest and capital and the likelihood of default. Bond markets existed for at least 60 years prior to advent of the first credit rating agency in 1909. There are approximately 150 credit rating agencies worldwide although just three agencies dominate; Moody’s, Standard & Poor’s and Fitch. Of these S&P and Moody’s dominate.

The next largest rating agency after S&P and Moody’s is Fitch. Fitch Publishing Company was founded in 1913 by John Knowles Fitch, a 33-year-old entrepreneur who had just taken over his father’s printing business. Fitch had a unique goal for his company: to publish financial statistics on stocks and bonds. In 1924, Fitch expanded the services of his business by creating a system for rating debt instruments based on the company’s ability to repay their obligations. Fitch were first to introduce the now familiar "AAA" to "D" ratings scale to meet the growing demand for independent analysis of financial securities. Although Fitch’s rating system of grading debt instruments became the standard for other credit rating agencies, Fitch is now the smallest of the “big three” firms.

Source: Basel Committee on Banking Supervision, 2000; Langohr and Langohr, 2008, p.384

(9) African bank credit rating

Note that the rating was investment grade on the national scale but sub-investment grade on a global scale.

(10) Regulatory role of credit rating agencies

The regulatory role of credit rating agencies dates back to 1934 when US bank regulators decreed banks could not invest in non-investment grade (low credit quality) bonds as determined by a recognised rating agency (or manual as they were then). The only recognised credit rating manuals at the time were those issued by Moody’s, Poor’s, Fitch and Standard. With this, the credit assessments made by these third-party and for-profit organisations attained force of law.

In the decades that followed, some insurance and pensions regulators followed similar routes by linking what they deemed suitable bond investments to the bond ratings issued by these rating agencies.

The SEC then finally cemented the regulatory role of rating agencies in 1975 with two acts:

- the creation of the ‘nationally recognised statistical rating organisation’ (NRSRO) and

- the grandfathering of Standard & Poor’s, Moody’s and Fitch as NRSRO’s,

by decreeing that only a rating from an NRSRO could be used as a measure of the credit-riskiness of broker-dealers (in 1975) and as a measure of the credit-riskiness of commercial paper held by money-market funds (1990).

Between 1975 and 2000, the SEC designated only four additional firms as NRSROs: Duff & Phelps in 1982; McCarthy, Crisanti & Maffei in 1983; IBCA in 1991; and Thomson BankWatch in 1992. Mergers among the entrants caused the number of NRSROs to return to the original three by year-end 2000.

Source: Journal of Economic Perspectives – Volume 24, Number 2- Spring 2010 – Pages 211-226. The Credit Rating Agencies by Lawrence J. White, Professor of Economics, Stern School of Business, New York University, New York. Lwhite@stern.nyu.edu.

(11) Reasons for moving from ‘investor pays’ to ‘issuer pays’

Some of the reasons given include:

- The advent of the high-speed photocopying machine meant that many investors would have been able to simply photocopy the rating manual from other investors thereby reducing the rating agencies’ revenue.

- The bankruptcy of Penn-Central Railroad in 1970 was a catalyst for bond issuers to have credit rating agencies vouch for them by rating their debt and reassuring investors about their low risk status. This made bond issuers more willing to pay for the rating.

- Bond rating firms realised, somewhat belatedly, that regulations had been passed in the US requiring some institutional investors to hold debt with certain ratings. This observation would make issuers more willing to pay for a rating to get their bonds into those portfolios.

(12) Press Release: FSB publishes thematic peer review report on reducing reliance on credit rating agency (CRA) ratings http://www.financialstabilityboard.org/press/pr_140512.htm

(13) Penn-Central Railroad

In May 1970, Penn Central Railroad, then on the verge of bankruptcy, appealed to the Federal Reserve for aid on the grounds that it provided crucial national defence transportation services. The Nixon administration and the Federal Reserve supported providing financial assistance to Penn Central, but Congress refused to adopt the measure. Penn Central declared bankruptcy on June 21, 1970, which freed the corporation from its commercial paper obligations. To counteract the devastating ripple effects to the money market, the Federal Reserve Board told commercial banks it would provide the reserves needed to allow them to meet the credit needs of their customers. In 1971, the government provided $676.3 million in loan guarantees. In 1976, the federal government consolidated the still struggling Penn Central with five other railroad companies that were also failing to form Consolidated Rail, or Conrail. The government spent $19.7 billion, including roughly $7.7 billion for the initial investment, to keep Conrail operating. By 1981, Conrail began to earn a profit. The government sold Conrail in 1987 for $3.1 billion. In addition to the sale price, the Treasury received a $579 million dividend from Conrail.

Source: http://www.propublica.org/special/bailout-aftermaths

(14) Norden, L. and Weber, M. (2004) Informational Efficiency of Credit Default Swaps and Stock Markets: The Impact of Credit Rating Announcements. Journal of Banking and Finance, 28, 2565-2573. http://dx.doi.org/10.1016/j.jbankfin.2004.06.011

(15) Hull, J., Predescu, M. and White, A. (2004) The Relationship between Credit Default Swap Spreads, Bond Yields, and Credit Rating Announcements. Journal of Banking and Finance 28, 2789-2811. http://dx.doi.org/10.1016/j.jbankfin.2004.06.010

Note that in emerging markets (like South Africa), the usefulness of credit spreads can be more limited than in developed markets, due to the lack of a readily liquid secondary market. Furthermore some methodologies used by exchanges to mark corporate bonds to market require a minimum traded amount to be reached before prices (and hence credit spreads) are updated.

(16) Hull, J., Predescu, M. and White, A. (2004) The Relationship between Credit Default Swap Spreads, Bond Yields, and Credit Rating Announcements. Journal of Banking and Finance 28, 2789-2811. http://dx.doi.org/10.1016/j.jbankfin.2004.06.010

(17) Response of rating agencies to criticisms of their discrete scales

Moody’s and Standard &; Poor’s have adopted refined rating categories by adding modifiers (e.g. “+” and “−”, or “1”, “2”, and “3”) to the generic rating categories to indicate whether a bond is on the upper, middle, or lower end of the rating category. The refinement of the rating categories can be viewed as a step moving from a discrete rating system toward a continuous spectrum. So refined ratings not only reflect the default probability more precisely, they also may trigger a rating change more quickly as rating agencies do not have to wait until the financial positions of bond issuers to deteriorate (or improve) to the next broader generic rating category to make rating changes.

Source: Do Credit Rating Agencies Sacrifice Timeliness by Pursuing Rating Stability? Evidence from Equity Market Reactions to CreditWatch Events Jenny Gu, Jeffrey S. Jones, Pu Liu, 13 May 2014

South African LDI: Banking on higher returns?

2014-06-17T23:47:00.004-07:00

By Shalin Bhagwan – Head: Institutional Solutions and Jana Kershaw – Credit Fund Manager and Analyst at Ashburton Investments

Ashburton Investments considers the inclusion of bank-issued inflation-linked paper instead of government-issued linkers (inflation-linked bonds) when constructing Liability Driven Investment (LDI) portfolios.

Since the issue of the first inflation-linked South African government bond in March 2000, the universe of inflation-linked bonds available in South Africa has long since expanded beyond just government bonds. As at the end of May 2014, there were 9 issues of government inflation-linked bonds listed on the Johannesburg Stock Exchange (JSE) with a market value of R369.3bn. This compares to 10 non-government issuers (excluding special purpose vehicles and notes) of listed inflation-linked bonds with an outstanding market value of R72.1bn across 38 issues. Within the non-government issuers of inflation-linked bonds, the local banks are the largest issuers. Their total issuance of R38.3bn across 65 issues amounts to 53% of non-government issuance and exceeds that of even the State Owned Companies (SOCs). By comparison, the SOCs have issued inflation-linked bonds with a market value of R33.4bn across 9 issues.

Some LDI portfolio managers have been buying bank-issued inflation-linked bonds and in this context it is important for clients to be aware that their de-risking portfolios contain credit risk. Taking credit risk in the LDI portfolio is not without international precedent but clients and advisers should asses the credit skill, experience and business model of the LDI manager before allowing the manager flexibility to include credit in the LDI portfolio.

1. An inflation-linked bond (or linker) is said to be an accreting asset.

What this means is that both the coupons and the final redemption payment increase in line with the reference inflation rate over time. This differs from a fixed interest bond.

By way of example consider two bonds:

Bond 1	7-year fixed bond maturing 31.12.2020, issued at par (R100) with a fixed coupon of 9% p.a.
Bond 2	7-year inflation-linked bond maturing 31.12.2020, issued at par with a real coupon of 5.75% p.a.

Assume that inflation runs at 6% p.a. over the period to maturity of these bonds. Expected cashflows on these bonds are illustrated in the chart below.

Source: Ashburton Investments

The key point is that in 2020 the fixed bonds pays you ZAR109 while the linker expects to pay you ZAR159, a difference of 45%. The accreting nature, especially of the final redemption payment, is the source of this difference.

2. So, inflation-linked bondholders expect their invested capital to be returned later than if they had invested in a comparable fixed-rate bond.

Bond issuers understand the accreting nature of an inflation-linked asset and that this feature means that bondholders effectively lend the bond issuer money for longer (as shown by our chart above). In the case of utilities or infrastructure providers, such as Eskom and SANRAL, issuing inflation-linked bonds to fund their borrowing requirements makes sense since their revenues are in part linked to inflation. There is good grounding in corporate finance theory to suggest that businesses with inflation-linked revenues can actually reduce the volatility of their net profits by having some of their borrowings as inflation-linked rather than fixed rate debt.

The fundamental case for banks issuing inflation-linked debt is less clear although banks are probably best placed to understand the risks and rewards of doing so. With sophisticated treasury teams, banks are able to hedge any risks posed to them from the issuance of such debt whilst ensuring that the favourable economics on offer, such as being able to capture greater borrowing for longer, is retained.

It is perhaps not surprising then that, outside of the government, banks are one of the largest issuers of inflation-linked bonds in South Africa. The chart shows the total size of all non-government issuers inflation-linked bond programmes across all maturities and issues. Apart from Eskom, SANRAL and the Trans Caledon Tunnel Authority (TCTA) the other main issuers of inflation-linked bonds are the banks. The aggregate issuance by the banks is greater than the aggregate issuance of Eskom, SANRAL and the TCTA.

Source: JSE

Given the premium placed on inflation-linked assets by local LDI investors and their simultaneous need for acceptable returns for their members, issuing inflation-linked bonds for credit risky borrowers can be a ‘win-win’ for both the issuer and the buyer. The credit-risky borrower is forced to issue the linker at a higher real yield than would be implied by a comparable government inflation-linked bond. The additional yield is compensation for the additional credit risk that the investor is taking but helps investors meet their return objectives in the current low-yield environment.

That said, we would argue that, fundamental to the ‘win-win’ premise is a thorough understanding of the risks involved which, due to the longer payback period, is even greater than may be immediately obvious.

3. For the most part South African LDI mandates own government and explicitly government-guaranteed inflation-linked bonds

Historically, the main non-government issuers of inflation-linked paper have been Eskom, SANRAL and TCTA. These are all explicitly guaranteed by the government of South Africa. For example, the SANRAL and Eskom guarantees are basically the same where the government becomes responsible for payment in the event that SANRAL or Eskom cannot repay the debt. Government has 20 and 5 business days to pay in the case of SANRAL and Eskom. Each bondholder can enforce their rights separately against the government. The TCTA guarantee calls for immediate payment if they fail to meet their obligations.

4. Bank issued inflation-linked bonds present a very different credit risk profile to their government and government-guaranteed counterparts and should be evaluated differently before inclusion in an LDI portfolio.

Clearly a thorough credit analysis must be carried out prior to investing in bank-issued inflation-linked bonds. More than that, the long-term nature of the funding being provided by the debt holder should be understood and properly compensated for.

This is critical in an LDI portfolio which is designed to be a hedging portfolio. A hedging portfolio should be designed to mitigate risk. This should extend to mitigating risk in stressed market conditions.

A further risk is liquidity risk. Since bank issued bonds tend to be of a smaller size, trading in the secondary market is much more limited and bid-offer spreads (the costs of buying and selling) are higher than government and government-guaranteed inflation-linked bonds.

As an example, the second chart above illustrates the difference one could expect in liquidity by considering the size of the various (non-government) issuers total inflation-linked bond programmes across all maturities and issues. In general, all else being equal, the larger the amount of stock in issue, the more liquid the bond. Other features do, however also impact liquidity.

The total size of bank issues (per issuer) range between 27% - 46% of the total issue size of government-guaranteed Eskom bonds. This feature combined with the government guarantee on Eskom bonds makes the latter far more liquid than the bank bonds.

5. LDI portfolios which include inflation-linked bonds issued by banks (and other credit-risky borrowers) should ensure that they have evaluated the risks of doing so.

The risks of holding a bank bond include both credit and liquidity risk as explained above. . Moody’s, in a report on the South African banking system, released in May 2014, noted that the outlook for the local banking sector remains negative – mainly the result of subdued economic growth, which will continue to exert pressure on asset quality. We have also seen recent negative rating action on some South African banks, and rating outlooks, in most cases, remain negative. In our view, this places any decision to hold non-government guaranteed bonds in the LDI portfolio firmly in the spotlight.

To demonstrate that these issues are not purely theoretical, the cashflow analysis we have shown above is broadly based on an actual bank issued inflation-linked bond maturing in 2020 and that also carries a coupon of 5.75%.

Holders of South African banks’ inflation-linked bonds, especially where held as part of a de-risking or LDI programmes, should ensure that their managers have the skill and credit analysis experience necessary in order to evaluate the risk-reward trade-off from holding these bonds.

6. Is a pension fund’s investment decision to increase their strategic exposure to inflation-linked bonds consistent with buying short-dated credit risky bonds?

Increasing the pension fund’s exposure to inflation-linked assets may be considered to be important for strategic reasons. In that context, buyers of credit-risky bank bonds should also consider the additional inflation-linked exposure being gained from buying the credit-risky inflation-linked bond. For bonds maturing in the next 3-7 years the additional duration provided is likely to be very small.

Returning to our example of the South African bank’s bond maturing in 2020, holders of this bond are only adding approximately 5 years of (modified) duration to the LDI portfolio. This should be compared to a modified duration of approximately 5 times as much (25 years) on the longest dated inflation-linked bond in the South African market.

Of course, the counterargument may be that the scheme requires some of these shorter dated bonds for cashflow reasons. We would argue that even then, consideration should be given to the likelihood that the bond repayments do not materialise as expected in which case the pension fund could be a forced seller of other assets at an inopportune time.

All these considerations (and others) should be taken into account when assessing the additional yield being earned on the credit-risky bond. For example, the 2020 bank issued inflation-linked bond in our example offered a real yield of 5.89% at the end of May, well in excess of real yields available on bonds from other banks. Comparable inflation-linked bonds from other banks were, at the time, trading at a real yield that was closer to 2%. Comparable government inflation-linked, by comparison were offering real yields closer to 1%p.a. Clearly, the additional yield offered by bank bonds relative to government bonds appears attractive but is it sufficient? Furthermore, within bank bonds further analysis, grounded in sound credit due-diligence, is necessary in evaluating the relative value of inflation-linked bonds across the different bank issuers.

7. The investment decision to increase the pension fund’s strategic exposure to inflation-linked bonds, could, in our view, be separated from the decision to take credit risk….

If the pension fund wished to buy a short-dated inflation-linked bond to pay cashflows, for example, it may consider buying the government inflation-linked bond maturing in 2017 but which (at the end of May) offered a real yield of only 0.9% p.a. For funds that are comfortable awarding their LDI manager a degree of flexibility then the LDI manager could access a wider toolkit to enhance the yield on the purchase of this bond.

This wider toolkit may include the use of credit-risky bonds and would allow the manager flexibility to synthetically create the exposure to the inflation-linked bond and use the cash to invest in a credit-risky bond which then need not be inflation-linked.

8. ...and credit risk, in turn, should be further decomposed to ensure a proper diversification of idiosyncratic credit risk as well as some consideration of the asymmetric nature of credit risk.

When investing in credit one of the commonly held tenets is to diversify idiosyncratic risk by ensuring exposure to a wide spectrum of issuers. By synthetically creating the inflation-linked bond exposure, the manager is free to diversify the credit-risky bond portfolio across different issuers (irrespective of whether the bond itself was inflation-linked) and in so doing avoid concentration risk to a single issuer.

The caveat here is that the South African listed corporate bond market is concentrated and so the benefits of this diversification are limited. As an example, there are only 52 different companies with listed bonds. Even if we extend our definition of credit, beyond listed bonds, to include loans to companies that have been syndicated by the banks making those loans, our estimate is that the average South African investor is only able to access no more than 100 different South African companies. This is in stark contrast to the world’s most liquid corporate bond market, the USA, which has more than 600 different issuers.

With the nascent state of our corporate bond markets, achieving further diversification requires investors to consider the unlisted debt space as an avenue for obtaining credit risk exposure to a wider spectrum of South African companies. The unlisted market could, in our view, assist South African investors in their diversification efforts by offering access to, at least, a further 400 different companies.

The second commonly held tenet is that any consideration of credit risk should include a proper evaluation of the asymmetrical risk-return profile of corporate bonds. By ‘asymmetric’ we mean that, unlike equities, corporate bonds have limited upside and unlimited downside. ‘Limited upside’ means that the maximum annual return is known at the outset and is defined by the gross redemption yield on the bond. This is in contrast to equities where the potential for capital gain is, in theory, unlimited. But both corporate bonds and equities have ‘unlimited downside’. That is, as with an equity investment, investors in corporate bonds can lose their entire investment.

To properly account for the asymmetric nature of their investment in credit, investors should expect their manager to also consider the risks associated with default and the subsequent likelihood of recovering if not all, then at least some, of their initial investment. This then leads to the concept of secured credit vs. unsecured credit.

Listed corporate bonds are generally unsecured in nature. Diversifying into unlisted credit can allow investors to access secured credit assets which, in turn, can offer investors greater protection should the credit worthiness of the borrower deteriorate.

9. It is a fallacy to believe that because an LDI portfolio’s holding in a credit-risky bank issue is only a small part of the overall LDI portfolio, that diversification of idiosyncratic credit risk has taken place.

A bond-only LDI portfolio may be stocked with government and government-guaranteed bonds. Adding a relatively small holding in a single name bank bond such as our example above may be considered prudent since the holding is small relative to the total LDI portfolio. Is this really the case?

In our view, a more appropriate way of evaluating the holding would be as set out below:

For the pension fund to consider what total exposure it would like to credit as an asset class (through a risk budgeting and strategic asset allocation exercise which could attempt to allow for differences in the risk-return profile of different credit instruments e.g. listed vs unlisted; secured vs unsecured).

To then control for idiosyncratic risk by setting appropriate parameters such as issuer, issue and sector concentration limits.

Not to follow such an approach raises the risk that the pension fund's portfolio could be overexposed to a specific issuer or sector at portfolio level. This could arise since the fund’s other asset managers (i.e. those not managing the LDI portfolio) could also be exposed to the same credit-risky issuer through their portfolios which hold their equity or indeed their bonds.

Key investor takeaway

When drafting the investment guidelines for an LDI mandate, clients should set explicit parameters around the types of inflation-linked bonds that the manager is permitted to include. For managers with appropriately skilled credit teams, allowing the manager to buy credit-risky inflation linked bonds could enhance the yield on the LDI portfolio. However, even then clear limits should be agreed with the LDI manager and those limits should be set based on the pension fund’s risk budget. Limits could include:

The percentage of the LDI portfolio that could be invested in credit–risky bonds.
Single issue limits.
Single issuer/obligor limits.
The exclusion of certain issuers.

Shalin Bhagwan, Head of Solutions

Shalin is a qualified actuary and responsible for Liability Driven Investment (LDI) and other risk management solutions for institutional clients including pension fund and insurers. He was previously Head of UK LDI for AXA Investment Managers. Prior to AXA, Shalin spent 5 years as Head of Structuring in the LDI team at Legal & General Investment Management, where he helped build both the UK and US LDI businesses, working out of London and Chicago. Shalin has previously worked as a pensions and investment consultant for Mercer and Aon Hewitt in London. Prior to leaving South Africa in 2003, Shalin was part of the Executive Committee in Sanlam’s pensions business.

Jana Kershaw, Credit Fund Manager and Analyst

Jana has over 10 years of experience in credit, including six years at RMB as a sell-side credit research analyst specialising in listed and unlisted debt issuers in South Africa. Prior to that, she spent four years at the Old Mutual Investment Group and Old Mutual Specialised Finance focusing on structured and high-yield debt. Jana was ranked number one credit analyst in South Africa by the Financial Mail for three consecutive years. RMB’s team, Jana Kershaw and Elena Ilkova were also ranked number one credit analyst team in the Financial Mail/JSE Spire Awards in 2012.

The most influential actuary of the last decade?

2013-12-29T00:14:00.002-08:00

Of couples and copulas

By Sam Jones
Published: April 24 2009 22:04 | Last updated: April 24 2009 22:04

Johnny Cash and June Carter met backstage at the Grand Ole Opry. It was a little like a country song: he was married, she recently divorced, and an affair ensued; both singers had young children, and Cash would have three more with his first wife before she left him in 1966, citing his drinking and carousing. Two years later, he proposed to Carter on stage and, despite having turned him down numerous times before, she accepted. They’d each found a life match.
It ended like a country song, too. In 2003, Carter died in Nashville of complications from heart surgery, and Cash followed her to the grave four months later. The heart complication for him, it seemed, was that it was broken: “It hurts so bad,” he told the audience at the last concert he would give. The pain, he said, tuning his guitar, close to tears, was “the big one. It’s the biggest.”

Cash was speaking for many a bereaved partner – and well before Johnny ever met June, scientists had noticed that cases of spouses dying in rapid succession were not at all unusual. By the 1980s, medical researchers had started writing about “stress cardiomyopathy”, or “apical ballooning syndrome”, the ungainly name for the peculiar condition whereby an individual’s brain, following an intense emotional trauma, would inexplicably release chemicals into the bloodstream that weakened the heart – in some cases, causing it quite literally to break.

The medical community was interested because it offered a chance, potentially, to intervene and prolong life. Another industry was interested in the phenomenon, too – but less to stop it and more to understand it. These were the actuaries working in life assurance. Actuarial science is the study of the statistics surrounding life and death – and the statistics surrounding the broken heart phenomenon were striking. Pages and pages of death records showed the same marked trend: that in human couples, the death of one partner significantly increases the chances of the death of the other. Dying of a broken heart – in the most general sense, not necessarily from stress cardiomyopathy – was not a rare occurrence, but something of a statistical probability. So much so that life assurers, in order to conduct their business, needed to incorporate it into their models. In a March 2008 study, Jaap Spreeuw and Xu Wang of the Cass Business School observed that in the year following a loved one’s death, women were more than twice as likely to die than normal, and men more than six times as likely. “This implies … that joint life annuities [in which payments continue at the same price until both partners die] are underpriced while last survivor annuities [in which payments increase after one partner dies] are overpriced,” concluded the authors.

Even before the definitive Cass study, however, actuaries had begun to incorporate the broken heart trend into their mathematical models calculating the chances of clients dying. How could such an ephemeral relationship be reliably captured? The actuaries, of course, relied on probability. While they could not hope to devise a model that predicted the likelihood of death from a broken heart for a specific couple, they could use statistical science to devise a fairly accurate picture across a group of people.

They borrowed from physics and devised a formula based on something called a Markov chain: a way of expressing a series of statistical events whose outcomes are dependent upon one another. In physics, Markov chain processes underlie our most basic understanding of the world around us, from the way liquid turns to gas to the way a drop of vivid ink might slowly diffuse through a glass of water. If you treated people like atoms, the actuaries reasoned, you could apply the same maths.

In the autumn of 1987, the man who would become the world’s most influential actuary landed in Canada on a flight from China. Neither Xiang Lin Li nor the handful of fellow junior academics with whom he was travelling – all from the University of Nankai – had ever been abroad before, yet they had come at the behest of the Chinese and Canadian governments to do something most unusual: study capitalism. The small band of mathematicians and statisticians would be taking business degrees at Quebec’s Laval University.

For Li, going to Canada was just the latest in a series of unlikely opportunities that had shaped his life up to then. Decades earlier, his family had suffered at the blunt end of Mao’s cultural revolution: his father, a mid-ranking police official, was precisely the kind of lowly bureaucrat that the red guard mob was intent on re-educating, and the family was uprooted to a small village in southern China. In the countryside, the chances of young Xiang Lin going to school – let alone university – were slim. But he was talented and driven, and made it not only into school, but on to Nankai, one of the country’s most prestigious institutions. Li studied economics, and had just passed his master’s examinations when the Canadians came calling. Determined to be among those sent to Quebec, he learnt French in four months – as much at home studying the language, it seemed, as he was crunching numbers.

Li’s drive did not abate abroad. Four years after arriving in Canada, he’d earned his MBA; by this stage, he had no intention of returning home. In the few years he had been away, China’s mini-glasnost period had withered. Hu Yaobang, general secretary of the Communist party and a pro-democratic reformist, had been ousted, and Chinese leader Deng Xiaoping was now wary of the liberalism genie that had been let out of the bottle. In 1989, the world had looked on as students were mowed down in Tiananmen Square. Universities such as Nankai were not exactly the safest places for ambitious young students – especially not ones returning from MBA courses in the west.
As if to make clear the break, Xiang Lin changed his name and became David Li. After graduation from Laval, he enrolled at a new university, Waterloo, near Toronto. He would now be studying actuarial science. And this wasn’t the only change: the move from genteel, francophone Montreal to the more worldly and business-oriented Toronto was profound – and deliberate. According to Jie Dai, a fellow immigrant from China and a classmate at Laval, “I clearly remember [Li] mention that if you are an actuarial guy, you can earn a lot of money.”

The big money in the 1990s, of course, was not to be found at Waterloo but in Silicon Valley, on Wall Street and in the City of London. The first of these might have been an obvious destination for a talented mathematician, but the latter two were also becoming magnets for the likes of Li. In 1984, Robert Rubin, who a decade later would become US treasury secretary, made a bold decision for his employer at the time, the investment bank Goldman Sachs. Rubin decided to hire Fischer Black, an economist and academic at the Massachusetts Institute of Technology’s Sloan School of Management. Prior to 1983, a few academics had toyed with economics and markets, but as intellectual curiosities; Black was the first of his kind – a serious academic, with publications under his belt and a tenured position to boot – to make the move to Wall Street, putting theory into practice and risking the scorn of his ivory tower colleagues.
Rubin’s bet earned Goldman many multiples of Black’s salary. At the bank, the professor pioneered the use of mathematics in pursuit of money. He was one half of the duo that came up with the Black-Scholes formula, which revolutionised Wall Street by promising to determine a rational price for market risks – a principle that would become the founding doctrine of a new field, quantitative finance. Quantitative finance’s practitioners were trying to outwit the markets, using maths to eliminate risk by first using maths to calculate it. And the numbers of those practitioners grew quickly. With the collapse of the Soviet Union, the end of the arms race and, in 1993, the cancellation by the US congress of the superconducting super collider – intended to be the world’s greatest physics experiment – particle physicists, experts in quantum mechanics and computing engineers were twiddling their thumbs. For the younger generation of newly qualified grads and PhDs, applying their expertise to finance was the obvious alternative to fighting it out for the dwindling number of jobs strictly in their fields.
Emanuel Derman – a particle physicist – was such a convert. He joined Goldman in 1985, working under Black before eventually taking over from his mentor, and recalls members of the “quant” influx being referred to as “POWs” – physicists on Wall Street. Equally accurate was the acronym used by Andrew Lo, another Wall Street quant and now a lecturer at MIT. What Wall Street was really after, said Lo in a recent lecture, was not PhDs, but PSDs: people who were “poor, smart and with a deep desire to get rich”. At Waterloo, Li fitted that description to a T. He was studying for his PhD in actuarial science, but no one expected him to go on to a career in academia. Instead, in 1997, after earning his doctorate, he took a job at one of Canada’s largest banks, Canadian Imperial Bank of Commerce (CIBC).
For graduates such as Li, joining the rough and tumble world of business could be something of a shock, even when armed with MBAs. At best, the mathematicians were semi-disparagingly referred to as quants; if they were lucky, trader colleagues might slap them on the back and call them, half in flattery, “rocket scientists”. Emanuel Derman recalls a time at Goldman Sachs when he and another quant colleague were standing on the trading floor, on either side of a central aisle, and a senior trader passed between them. The trader “winced, clutching his head with both hands as though in excruciating pain, and exclaimed, ‘Aaarrggh-hhh! The force field! It’s too intense! Let me out of the way!’”
And yet by the time Li got to New York, in 1998, the quants had taken over the asylum. In the summer of that year, Long Term Capital Management, a hedge fund run by the finest minds in quantitative finance, required a massive bailout from the federal government. But far from serving as a warning that mathematical models could get investors into serious trouble, LTCM exploded the notion of quantitative finance as a geeky, back-office support task. The fund’s might before its fall – and the fact that its failure might have left a trillion-dollar hole in the financial system – discounted the notion that traders’ instinct and experience counted more than numerical intelligence.
The quants weren’t exactly out on the trading floor, however. The best of them still spent their days writing papers, crunching numbers, applying their academic expertise to the world of business. Li had come to New York to work for a consultancy called the RiskMetrics Group, which had been spun out of JP Morgan, but he was still thinking about life, death and love. In 2000, he published a paper in the prestigious Journal of Fixed Income that gained some serious attention. In it, Li performed a most elegant trick. Borrowing from his work in actuarial science and insurance and his knowledge of the broken-heart syndrome, he attempted to solve one of Wall Street quants’ most intractable problems: default correlation.

Markets do not function in laboratory-like isolation. They are linked, correlated. It isn’t enough for any quant to try and know the probability of each individual company in his bank’s portfolio going bust; he has to know how the bankruptcy of one company – or several – might increase (or decrease) the likelihood that other companies will default. Suppose, for example, that a bank loans money to two outfits – a dairy farm and a dairy. The farm, according to ratings agencies, has a 10 per cent chance of going bust and the dairy a 5 per cent chance. But if the farm does go under, the chances that the dairy will follow will rise above 5 per cent – quickly and steeply – if the farm was its main milk supplier.
And it gets more complicated from there. How correlated are the default probabilities on bonds issued by our Irish dairy farm and those issued by a software company in Malaysia? Not at all, you might think: the businesses not only provide totally different products and services, they’re also geographically remote from each other. Suppose, though, that both companies have been lent money by the same troubled bank that is now calling in its loans.
In fact, this is exactly what sank LTCM. How correlated are Russian government bonds and those in Mexico? Not at all, according to LTCM’s model, which, it should be noted, crunched data going back a hundred years. And yet it turned out for the hedge fund that both markets were dominated by the same few investors. The 1998 financial crisis in Russia, when Boris Yeltsin’s government defaulted on its bonds, caused panic selling in Mexico as investors rushed to de-risk their portfolios.
Li realised that his insight was groundbreaking. Speaking to The Wall Street Journal seven years later, he said: “Suddenly I thought that the problem I was trying to solve [as an actuary] was exactly the problem these guys were trying to solve. Default [on a loan] is like the death of a company.” And if he could apply the broken hearts maths to broken companies, he’d have a way of mathematically modelling the effect that one company’s default would have on the chance of default for others.

When mathematicians and physicists want to describe the chances of events occurring, they often rely on a curve called a copula. The Latin root is a noun meaning a “link or tie”, and indeed, copulas connect variables in such a way that their interdependence can be plotted. Throughout his PhD at Waterloo, and at CIBC, Li had been interested in how he could use copulas to develop existing actuarial models of the broken heart syndrome. The problem with relying on Markov chains was that they painted a far too mechanical, physical – atomic, even – picture of the human lifespan. Li reasoned that with a copula that showed a probable distribution of outcomes, a more accurate, encompassing picture of the broken heart or, for that matter, the broken company, could be devised.
He decided to use a very standard type of curve – the Gaussian copula, which is better known as a bell curve, or normal distribution – to map and determine the correlation on any given portfolio of assets. In the same way that actuaries could tell their employers the chances of Johnny Cash dying soon after June Carter without knowing anything about Cash other than the fact of his recent widowhood, so quants could tell their employers the effect one company defaulting might have on another doing the same – without knowing anything about the companies themselves. From this point on, it really could be, would be, a number-crunching game.
By 2003, Li’s paper had made his name on Wall Street. By now he was director and global head of derivatives research at Citigroup, and on a bright Tuesday morning in November, he arrived at the annual Quant Congress to bask in the glory with a presentation about his work. In front of a room of hundreds of fellow quants (“not a million miles from some kind of science fiction convention”, one person who was there recalls) he ran through his model – the Gaussian copula function for default.
The presentation was a riot of equations, mathematical lemmas, arching curves and matrices of numbers. The questions afterwards were deferential, technical. Li, it seemed, had found the final piece of a risk-management jigsaw that banks had been slowly piecing together since quants arrived on Wall Street.

By 2001, correlation was a big deal. A new fervour was gripping Wall Street – one almost as revolutionary as that which had struck when the Black-Scholes model brought about the explosion in stock options and derivatives in the early 1980s. This was structured finance, the culmination of two decades of quants on Wall Street. The basic idea was simple: that banks no longer had to hold on to risks. Instead they could value them, using complex maths and modelling, then package and trade them like any other, ordinary security.
Mortgages were the prime example. Rather than make a mortgage loan and gradually collect interest over its lifespan, banks began to bundle the loans together and sell them into specially created off-balance-sheet shell companies. These companies in turn issued bonds to raise cash. And by using the modelling and maths being cranked out by quants, banks were able to tailor the structure of mortgage portfolios to ensure that bonds of varying risks could be issued to investors. The problem, however, was correlation. The one thing any off-balance-sheet securitisation could not properly capture was the interrelatedness of all the hundreds of thousands of different mortgage loans they owned. As a consequence, structured finance had remained a niche and highly bespoke practice throughout the 1990s.

On August 10 2004, however, the rating agency Moody’s incorporated Li’s Gaussian copula default function formula into its rating methodology for collateralised debt obligations, the structured finance instruments that subsequently proved the nemesis of so many banks. Previously, Moody’s had insisted that CDOs meet a diversity score – that is, that each should contain different types of assets, such as commercial mortgages, student loans and credit card debts, as well as the popular subprime debt. This was standard investing good practice, where the best way to guard against risk is to avoid putting all your eggs in one basket. But Li’s formula meant Moody’s now had a model that enabled it to gauge the interrelatedness of risks – and that traditional good practice could be thrown out of the window, since risk could be measured with mathematical certainty. No need to spread your eggs across baskets if you knew the exact odds of your one basket being dropped. A week after Moody’s, the world’s other large rating agency, Standard & Poor’s, changed its methodology, too.
CDOs built solely out of subprime mortgage debt became the rage. And using the magic of the Gaussian copula correlation model, and some clever off-balance-sheet architecture, high-risk mortgages were re-packaged into triple-A-rated investor gold. The CDO market exploded. In 2000, the total number of CDOs issued were worth somewhere in the tens of billions of dollars. By 2007, two trillion dollars of CDO bonds had been issued. And with so many investors looking to put their money in debt, that debt became incredibly cheap, fuelling a massive boom in house prices and turbo-charging the world’s economies.

The unwinding started, as we all now know, in the US subprime housing market. Defaults started to increase in late 2006. The banks weren’t worried at first. Their models assumed that the pinprick default points all over the US were not correlated. But the defaults kept coming. By early 2007, it was clear that the US subprime market had a problem and by that summer, homeowners all over the US were defaulting on their mortgages. The cheap debt made available by the finance revolution was so cheap, in fact, that the loans should never have been made. And the correlation model was still mapping the housing market as it had been in 1990s, not the grossly inflated monster it had become. The development of the model had, ironically, changed the nature of the reality it was modelling.
The losses the banks began to take against their holdings of CDOs were staggering. And as the institutions grew fearful about one another’s solvency, they stopped lending to each other. Global liquidity dried up. The rot spread from asset class to asset class, and the banks’ pain spread to the real economy. Suddenly, everything was highly correlated.
How had Li’s formula failed to anticipate this? The problem was that it assumed events tended to cluster heavily around an average – a “normal” state. In actuarial science, Li’s formula could adequately capture binary outcomes such as life or death, but in the messy world of mortgages and economics, it faltered. The range of possible outcomes here was more complicated, and indeed, random, than those facing an insurance company’s clients. Markets – particularly the mortgage market – were far more prone to extreme correlation scenarios than were insurers. Death from a broken heart, for all its poetic associations, is far easier to predict than the more prosaic, but ultimately unknowable, interrelatedness of markets.

Why did no one notice the formula’s Achilles heel? Some did. Nassim Nicholas Taleb, author of the bestselling The Black Swan – a book about the importance of considering outliers when looking at copulas – was a voluble critic of quantitative finance and Li’s formula. “The thing never worked,” he says. “Anything that relies on correlation is charlatanism.”

In 2007, David Li left Wall Street and moved back to China. He could not be contacted for this story. But two years earlier, before the financial system blew up, he did warn: “Very few people understand the essence of the model.” Harry Panjer, a professor of statistics and actuarial science who was Li’s mentor at Waterloo, strikes a balance between Taleb’s accusations and the stance of Li. Earlier this year, Panjer told The Toronto Star newspaper: “We have a saying in statistics, ‘All models are wrong, but some are useful.’” And David Li’s model was, for a period, profoundly useful.

Read more »

Using LDI to set pension fund investment strategy

2013-07-03T07:17:00.000-07:00

What is LDI?

Table 1: Factors influencing asset and liability values

Factors influencing volatility of asset values	Factors influencing volatility of liability values
Systematic (or market) risk (e.g. equity market risk for equities or property market risk for property investments	Interest rate risk
Credit risk	Inflation risk
Liquidity risk	Longevity risk
Active manager risk	Model risk

Chart 1: Risk attribution with liabilities

Table 2: Brief description of factors influencing asset and liability values

Factors influencing volatility of asset and liability values	Description
Systematic (or market) risk (e.g. equity market risk for equities or property market risk for property investments	This is risk which characterises the asset class to which an investor is exposed. It cannot be diversified by holding a diversified portfolio of securities within the asset class.
Credit risk	The risk of losing some or all of your investment due to deterioration in the credit-worthiness of your counterparty. While it can be included as part of market risk, for some asset classes, such as corporate bonds, it can make sense to measure this separately due to its importance to the overall risk associated with the asset class. Some quantification of this risk can be made by observing historical, long-term average default rates.
Liquidity risk	The speed and cost at which an investment can be sold for cash.
Active manager risk	The risk that an active manager underperforms its benchmark. When considering its strategic asset allocation, a fund may assume that it can achieve certain market returns, however taking a decision to appoint an active manager means that the manager can underperform the market.
Interest rate risk	The risk that the value of the assets (or liabilities) changes in response to changes in interest rates. Fixed rate and inflation-linked bonds will typically be the highest contributors towards an asset portfolio’s interest rate risk. Liability cashflows are valued to today’s monetary terms by discounting them to the present day. An increase in the rate of interest used to discount these liabilities will decrease their present value and vice versa. An often quoted, but narrower definition of LDI, is that LDI is about constructing an investment strategy that better aligns the interest rate sensitivity of the assets to those of the liabilities.
Inflation risk	The risk that the value of the assets (or liabilities) changes in response to changes in the market’s expectations for future rates of inflation. Inflation-linked bonds will typically be the highest contributors towards an asset portfolio’s inflation risk. Liability cashflows are valued to today’s monetary terms by discounting them to the present day. Where future cashflows to be paid increase in line with inflation then an increase in expectations for future rates of inflation will increase the present value of these cashflows and vice versa. An often quoted, but narrower definition of LDI, is that LDI is about constructing an investment strategy that better aligns the inflation sensitivity of the assets to those of the liabilities.
Longevity risk	This is the risk that the liability cashflows to be paid in the future increases as a result of the recipients of those cashflows living longer than anticipated. Generally, deteriorating longevity (pensioner dying earlier than expected) is considered to be less of an issue as it does not lead to a strain on the finances of the pension fund.
Model risk	This is the risk that the actuary’s projection of future cashflows turns out to be lower than those cashflows that actually materialise. Key demographic assumptions are sometimes embedded in these cashflow estimates, for example, assumptions relating to spouses pensions and early or ill-health retirement experience. The pension fund’s actual experience is likely to differ from this.

What have been the catalysts for this focus on LDI?

Globally we can point to two main catalysts for extending the definition of risk and hence for the focus on LDI:

1. Accounting regulations:

Table 3: Pension liabilities and deficits (Source: Accounting for Pensions 2010; LCP)

Company	Pension liabilities	Pension deficit	Market capitalisation	Liabilities / Market cap	Deficit / Market cap
British Airways	£12.8bn	£0.6bn	£1.6bn	791%	37%
British Telecom	£33.2bn	£4bn	£6bn	551%	66%

2. Funding regulations:

LDI in Africa

Future developments

Smoothing and the USA.....aka the Underfunded (Pension) Schemes of America?

2012-12-05T14:36:00.001-08:00

Lots of talk this morning about the smoothing of discount rates. You already know all of the following i.e.

The FT article and the market moves as outlined below

7bps sell off in long-end gilts (i.e. higher gilt yields)
4bps sell off in swaps (i.e. higher swap yields but clearly swaps not being hurt as much gilts)
3bps tightening of swap spreads (so you get more juice versus LIBOR when you asset swap your gilt)
2bps sell-off in breakevens (i.e. RPI-only hedging is cheaper by at least 2bps as a result of linker real yields selling off more than nominal gilt yields)

http://cdn.hm-treasury.gov.uk/autumn_statement_2012_complete.pdf (page 44 - 1.137)

But……for me this morning’s news brings back memories of 2008, when I was getting myself acquainted with the US LDI market, and came across the letter below to congress by global actuarial firms calling for a smoothing of discount rates (page 2 last bullet point). As an astute observer remarked, this was written almost 4 years to the day that we have this announcement about the consultation on “smoothing” in the UK.

Now, of course, we must recognise that those were very different times and in a different geography, nevertheless in a market that is thirsting for information which may give clues to how the various stakeholders in UK pensions may respond, I thought this was interesting enough to be shared.

Of course, the story doesn’t end there because this year the US enacted MAP21 which effectively converted the “temporary relief” in discount rates, proposed in 2008, into a more permanent one. I am not going to summarise MAP21 because I think the notes below from Aon Hewitt Ennis Knupp (AHEK), as they are known in the US, as well as Towers Watson do a far better job of this than I could but the implications are similar to those we witnessed in the Dutch market when the UFR was introduced, and in particular the steepening of the long-end Euro rates curve that accompanied the introduction of the UFR.

The key point to note from MAP 21 (when comparing to today’s announcement in the UK) is that MAP21 actually allows the discount rate to be a function of 25-year average rates. That’s not a typo – that’s twenty five years! There are lots of subtleties here which make a direct comparison to the UK more complex than is apparent at first sight, e.g. US discount rates are a function of corporate bond yields rather than Treasury yields, so the devil does lie in the detail but even this doesn’t change the fact that the impact of MAP 21 on discount rates is significant.

If we get anywhere close to MAP 21, we are all going to have start thinking about whether we choose to hedge the true economic liability or the “regulatory” liability. As the AHEK note summarises on page 9, the impact and course of action that any specific plan follows is going to depend on their own specific position with regard to (at least) 2 factors - “risk budget” and “solvency level”. It is also going to depend on the relative importance placed on “contribution stabilisation” versus looking through to the true economic position which will ultimately not change over the long-term.

Anyway, with a number of members of the Bhagwan household struck down with flu, and so “daddy duties” calling, I am not going to get the time tonight to give this topic the detailed analysis it deserves.

I hope the introduction into the local debate of what has happened across the Atlantic may serve as further food for thought and discussion over the coming days.

Finally, if the “LDI” market doesn’t have enough to contend with following this announcement then perhaps you will indulge me introducing two further "tail risk" variables which we may need to contend with come autumn 2014 - now that's what I call thinking ahead!

Those variables arise out of the possibility of a "yes" vote for Scottish independence in 2014. See article below by Mark Allan of AXA IM.

In particular, the possibility of gilts being retired and replaced by new Scottish issues as well as the size of Scottish banks vs. Scottish GDP. I think if I were “long" a diversified portfolio of OTC LDI hedges via a range of the common LDI banking counterparties, I wouldn’t want to have to deal with the additional complication of a positive vote for Scottish independence and what this may mean for my counterparty credit risk on some of those. If the vote takes place in late November, then perhaps we could conjure up a string of wins for the Scottish rugby team in the autumn 2014 internationals in the hope that the resulting boost to national pride will quell any desire to express that pride at the polls and hence preserve the unity of the Kingdom.

So much for winding down for Christmas, huh? Talking of Christmas, perhaps Santa has a truckload of goodies for all those naughty pension schemes who haven’t been hedging?

Letter to congress TW Funding relief AHEK MAP 21 Scottish independence SOA Funding Relief

RPI-CPI: Yeah but am I bovvered though?

2012-05-18T16:32:00.001-07:00

QE bad for pension funds; bad for the economy too? - Pension Funds Insider

2012-05-15T01:00:00.000-07:00

UK pension funds need derivatives rethink

2012-05-14T16:00:00.006-07:00

Roundtable: Liability-driven investment – planning ahead - Special Features - Pensions Week

2012-05-13T15:52:00.001-07:00

Roundtable: Liability Driven Investment – performance - Special Features - Pensions Week

2012-05-12T15:52:00.000-07:00

Consultants: Pensions, While Hesitant, Likely to Increase Derivative Use

2012-05-11T10:18:00.000-07:00

Greeks discount the value of LDI strategies

2012-05-06T17:30:00.001-07:00

Credit Downgrade Triggers 2.0

2012-04-27T15:49:00.001-07:00

Segregated pension schemes transacting OTC derivatives have often sought credit downgrade triggers against the counterparty banks with whom they are transacting. Banks are increasingly reluctant to offer these downgrade triggers. How should pension schemes respond?

Read more »

Convertible index-linked gilts? They're called "Maggie Mays".

2012-04-26T13:09:00.000-07:00

In 1983 with an election looming the then Conservative government issued an index-linked bond (the 2% 1999) with an embedded option allowing the holder to convert the bond into a nominal bond (10.25% 1999) at any one of three future dates. This is the first and only time a convertible index-linked gilt was issued.

Read more »

LDI

Is the US Dollar still a cheap tail risk hedge?

PPF Strategic Plan: an observation for private market investment managers

The Impact of Physical Climate Risk on CDI and Secured Income strategies

Could Jeff Bezos' Amazon solve the pensions conundrum?

Assessing the liquidity risk premium in government-guaranteed bonds

Monitoring credit risk in South African fixed income portfolios

The bluffers guide to covered bonds

2015 - are we there yet?

What do Tesco and tracker funds have in common?

Using LDI to set pension fund investment strategy

Guns don’t kill people, people kill people: lessons in money-lending from African Bank

(12) Press Release: FSB publishes thematic peer review report on reducing reliance on credit rating agency (CRA) ratings http://www.financialstabilityboard.org/press/pr_140512.htm

South African LDI: Banking on higher returns?

The most influential actuary of the last decade?

Using LDI to set pension fund investment strategy

Smoothing and the USA.....aka the Underfunded (Pension) Schemes of America?

RPI-CPI: Yeah but am I bovvered though?

QE bad for pension funds; bad for the economy too? - Pension Funds Insider

UK pension funds need derivatives rethink

Roundtable: Liability-driven investment – planning ahead - Special Features - Pensions Week

Roundtable: Liability Driven Investment – performance - Special Features - Pensions Week

Consultants: Pensions, While Hesitant, Likely to Increase Derivative Use

Greeks discount the value of LDI strategies

Credit Downgrade Triggers 2.0

Convertible index-linked gilts? They're called "Maggie Mays".